فازی

Open Access Article

1 - Fuzzy Evaluation of Long-Term Impacts of Electrical Appliances Efficiency Improvement on Load Pattern
M. Behrangrad M. Parsa-Moghaddam

20.1001.1.16823745.1386.5.12.8.9

Evaluation of long-term home electrical appliance efficiency improvement scenarios on load and energy pattern needs comprehensive and precise modeling taking to account a variety of uncertainties. In the modeling process, all effective parameters should be considered. E More

Evaluation of long-term home electrical appliance efficiency improvement scenarios on load and energy pattern needs comprehensive and precise modeling taking to account a variety of uncertainties. In the modeling process, all effective parameters should be considered. Estimation is the main source of information in this process which is a long-term large scale impact assessment procedure. Furthermore, the non homogeneous structure of load behavior in response to DSM policies makes the problem more sophisticated. The presented method implies fuzzy numbers to model the main uncertainties of the demand side reactions to the proposed DSM program. Here, the social classes of customers and their behaviors regarding energy utilization as well as time dependency of the problem parameters are taken into account. The paper focuses on the efficiency improvement of the electric appliances in Iran as a long-term DSM program, due to their considerable share in electricity consumption in residential sector. Finally, the numerical results are presented. Manuscript profile

Open Access Article

2 - An Intelligent BGSA Based Method for Feature Selection in a Persian Handwritten Digits Recognition System
N. Ghanbari S. M. Razavi S. H. Nabavi Karizi

20.1001.1.16823745.1390.9.1.4.1

In this paper, an intelligent feature selection method for recognition of Persian handwritten digits is presented. The fitness function associated with the error in the Persian handwritten digits recognition system is minimized, by selecting the appropriate features, us More

In this paper, an intelligent feature selection method for recognition of Persian handwritten digits is presented. The fitness function associated with the error in the Persian handwritten digits recognition system is minimized, by selecting the appropriate features, using binary gravitational search algorithm. Implementation results show that the use of intelligent methods is well able to choose the most effective features for this recognition system. The results of the proposed method in comparison with other similar methods based on genetic algorithm and binary particle method of optimizing indicates the effective performance of the proposed method. Manuscript profile

Open Access Article

3 - Designing Optimal Fuzzy Classifier Using Particle Swarm Optimization
Seyed-Hamid Zahiri

20.1001.1.16823745.1391.10.4.5.5

An important issue in designing a fuzzy classifier is setting its structural and mathematical fuzzy parameters (e.g., number of rules, antecedents, consequents, types and locations of membership functions). In fact, the variations of these parameters establish a wide More

An important issue in designing a fuzzy classifier is setting its structural and mathematical fuzzy parameters (e.g., number of rules, antecedents, consequents, types and locations of membership functions). In fact, the variations of these parameters establish a wide range high dimensional search space, which makes heuristic methods some suitable candidates to solve this problem (designing optimal fuzzy parameters). In this paper, a method is described for this purpose. In presented technique, all fuzzy parameters of a fuzzy classifier, are interpreted in structure of particles and PSO algorithm is employed to find the optimal one. Extensive experimental results on well-known benchmarks and practical pattern recognition problem (automatic target recognition) demonstrate the effectiveness of the proposed method. Manuscript profile

Open Access Article

4 - Implementation of a Fuzzy Multi-Agent Model for City Evacuation Traffic Management Using Probabilistic Automata
A. R. Karbaschian Saeed Setayeshi arash Sharifi

20.1001.1.16823745.1393.12.4.3.7

Because of importance of quickly city evacuation during natural or unnatural happenings, it’s essential to apply an optimized control policy to prevent congestion and stop of vehicles. Existing works for traffic management in critical conditions have paid little attenti More

Because of importance of quickly city evacuation during natural or unnatural happenings, it’s essential to apply an optimized control policy to prevent congestion and stop of vehicles. Existing works for traffic management in critical conditions have paid little attention to artificial intelligence approaches. Therefore, the main goal of authors in this research is offering an optimized and intelligent control policy for city evacuation traffic. This policy uses fuzzy inference system for decision making of each agent and probabilistic automata for optimizing performance of agents as for their preferences during time. To check degree of success of offered control policy, Agent Base Simulation in RStudio and Netlogo environments have been implemented using RNetlogo and frbs packages in R language. Simulation results show traffic load distribution, using maximum capacity of roads and congestion prevention by suggested policy. With regard to communication technologies such as GPS, smart phones, automatic tax payment systems in roads and … that have been developed in recent years, it is also possible to implement suggested critical traffic control policy in real world. Manuscript profile

Open Access Article

5 - Topology Control in Wireless Sensor Networks Using Two-Level Fuzzy Logic
A. Abdi Seyedkolaei A. Zakerolhosseini

20.1001.1.16823745.1393.12.4.8.2

Wireless sensor networks are a new generation of networks that from sensors uses to get information about itself environment and communication this sensors is as wireless. One of the issues that is very important in wireless sensor networks is Discussion reducing energy More

Wireless sensor networks are a new generation of networks that from sensors uses to get information about itself environment and communication this sensors is as wireless. One of the issues that is very important in wireless sensor networks is Discussion reducing energy consumption and increasing network lifetime. Topology control is one of the methods to reduce energy consumption and increase the lifetime of the network. Since different methods of topology control, to reduce energy consumption and enhance the network lifetime is proposed that including them is the clustering and one of the most famous clustering methods is LEACH. In this paper, we try to present a new clustering method that is superior compared to leach and other improved methods after the LEACH. we use in our clustering method from two-level fuzzy logic that be causing reduce energy consumption and increase the network lifetime compared to other methods and to prove the superiority of our method compared with other methods, we present a comparison using MATLAB software. Manuscript profile

Open Access Article

6 - Learners Grouping in Adaptive Learning Systems Using Fuzzy Grafting Clustering
M. S. Rezaei Gh. A. Montazer

20.1001.1.16823745.1394.13.4.5.6

Quality of adaptive and collaborative learning systems is related to appropriate specifying learners and accuracy of separation learners in homogenous and heterogeneous groups. In the proposed method for learners grouping, researchers effort to improving basic clusterin More

Quality of adaptive and collaborative learning systems is related to appropriate specifying learners and accuracy of separation learners in homogenous and heterogeneous groups. In the proposed method for learners grouping, researchers effort to improving basic clustering methods by combination of them and improving methods. This work makes the complexity of grouping methods increased and quality of result’s groups decreased. In this paper, new method for selection appropriate clusters based on fuzzy theory is proposed. In this method, each cluster is defined as a fuzzy set and the corresponding clusters are determined. So the best cluster is selected among each corresponding clusters. The results of an empirical evaluation of the proposed method based on two criteria: “Davies-Bouldin” and “Purity and Gathering” indicate that this method has better performance than other clustering methods such as FCM, K-means, hybrid clustering method (HCM), evolutionary fuzzy clustering (EFC) and ART neural network. Manuscript profile

Open Access Article

7 - Evaluation of Fuzzy-Vault-based Key Agreement Schemes in Wireless Body Area Networks Using the Fuzzy Analytical Hierarchy Process
M. Ebrahimi H. R. Ahmadi M. Abbasnejad Ara

20.1001.1.16823745.1394.13.4.6.7

Wireless body area networks (WBAN) may be deployed on each person’s body for pervasive and real time health monitoring. As WBANs deal with personal health data, securing the data during communication is essential. Therefore, enabling secure communication in this area ha More

Wireless body area networks (WBAN) may be deployed on each person’s body for pervasive and real time health monitoring. As WBANs deal with personal health data, securing the data during communication is essential. Therefore, enabling secure communication in this area has been considered as an important challenge. Due to the WBAN characteristics and constraints caused by the small size of the nodes, selection of the best key agreement scheme is very important. This paper intends to evaluate different key agreement schemes in WBANs and find the best one. To achieve this goal, three schemes from existing research named OPFKA, PSKA and ECG-IJS are considered and a fuzzy analytical hierarchy process (FAHP) method is employed to find the best scheme. Manuscript profile

Open Access Article

8 - Estimation of Causality Coefficients in Strategy Maps Using Gravitational Search-Based Learning of Fuzzy Cognitive Maps
A. Jahanbeigi A. Jahanbeigi M. Rohani

20.1001.1.16823745.1395.14.1.10.2

More than two decades ago, the balanced scorecard method was proposed to control and monitor the strategy of organizations. The most important outcome of this method is the strategy map. The causal relations among strategic goals (SGs) are established in this map which More

More than two decades ago, the balanced scorecard method was proposed to control and monitor the strategy of organizations. The most important outcome of this method is the strategy map. The causal relations among strategic goals (SGs) are established in this map which can help managers in decision making process. To have a precise strategy map, it is necessary to estimate the strengths of each causal relation correctly. So, the estimation of causal coefficients has attracted research interest in forming strategy maps. In this way, DEMATEL and Delphi are two well-known methods that are based on the experts’ opinion. However, these opinions are not exact in the complex business fields; so, the computational intelligence (CI) algorithms have been employed for more precise estimation of causality coefficients. In this study, the relations among SGs and their coefficients have been provided by the experts of a banking institution as the input of the proposed method. The main purpose of this study is to improve the precision of causal coefficients using a CI-based algorithm. For this purpose, the strategy map is decomposed into multiple fuzzy cognitive maps (FCMs) and then, the gravitational search algorithm (GSA) is employed for FCM training. In this way, two objective functions are used for determining the optimal value of causality coefficients. The first objective function is employed for reducing error in the prediction of SG realization percentages. The second objective function keeps causal coefficients in the intervals determined by the experts. Experimental results show that the total error of proposed model is lower than the expert-based model. In addition, GSA performs better than the following algorithms in finding the global optimum point in this real-world case study: particle swarm optimization and ant colony optimization. Manuscript profile

Open Access Article

9 - A Proposed Method of Decentralized Load Balancing Algorithm in Heterogeneous Cloud Environments
S. Hourali S. Jamali F. Hourali

20.1001.1.16823745.1395.14.2.16.0

One of the key strategies to improve the efficiency is load balancing. Choosing the appropriate VM to do any task, is function of various parameters such as the amount of required resources like CPU, memory, the size of VM resource, cost and maturity of VMs. In this pap More

One of the key strategies to improve the efficiency is load balancing. Choosing the appropriate VM to do any task, is function of various parameters such as the amount of required resources like CPU, memory, the size of VM resource, cost and maturity of VMs. In this paper, by considering each of these criteria and design objectives such as load balancing, reducing the rate of create new VM, and VM migration, we modeling the problem in terms of effective parameters in performance. Then, we solving this model by using the PROMETHEE method, which is one of the most widely used method for MADM problems. In this method, selecting the best VM occurs based on the value assigned to each of criteria which is calculated based on fuzzy logic. To evaluate the performance of this approach, the necessary simulations have been carried out on CloudSim simulator and shown that the proposed method has better performance compared to FIFO, DLB and WRR methods on average in terms of response time, rate of success tasks, load variation and rate of VM migration. Manuscript profile

Open Access Article

10 - Classification and Phishing Websites Detection by Fuzzy Rules and Modified Inclined Planes Optimization
M. Abdolrazzagh-Nezhad

20.1001.1.16823745.1395.14.4.12.0

One of the most important factors influencing the development of information technology on internet is steal the customer information. This security threat is known as phishing. With regarding to review and analysis of the published methods, lake of create the flexibili More

One of the most important factors influencing the development of information technology on internet is steal the customer information. This security threat is known as phishing. With regarding to review and analysis of the published methods, lake of create the flexibility to effective attribute selection in the procedure of phishing websites detection, non- dynamic behavior of classification algorithm on target websites and also no attention to reduce the amount of computation for the large number of websites are the main gaps of these methods. To achieve the above-mentioned objectives, a new dynamic mechanism is planned to flexible attribute reduction based on designing threshold change of assessment in this paper. Then inclined planes optimization algorithm is memorized based soft reducing the effect of the embedded memory though high iterations and 12 fuzzy rules are defined in a fuzzy inference system for intelligent dynamiting the algorithm. The experimental results of the proposed intelligent algorithm and the comparison the algorithms with the best available algorithms; demonstrate the ability of the modified inclined planes optimization algorithm to detect phishing websites and satisfy the above mentioned objectives. Manuscript profile

Open Access Article

11 - Symbolic Verification of Temporal Fuzzy Logic Properties on Fuzzy Program Graph
G. Sotudeh A. Movaghar

20.1001.1.16823745.1396.15.1.14.3

We may investigate the correctness of dynamic fuzzy models by a combination of Modal Temporal Logics and Fuzzy Logic. So far Fuzzy-extended Kripke structure (FzKripke) and Fuzzy-extended Program Graph (FzPG) are introduced as two timed Fuzzy logic models. Meanwhile, a F More

We may investigate the correctness of dynamic fuzzy models by a combination of Modal Temporal Logics and Fuzzy Logic. So far Fuzzy-extended Kripke structure (FzKripke) and Fuzzy-extended Program Graph (FzPG) are introduced as two timed Fuzzy logic models. Meanwhile, a Fuzzy-extended Temporal Logic (FzCTL) is introduced. Although no verification technique is devised for verifying FzCTL properties of timed Fuzzy logic models, its applications in verification of Fuzzy Logic Circuits (i.e., Fuzzy Flip-Flops) are studied and elaborated. In this paper we introduce a symbolic approach to tackle the state space explosion problem in timed Fuzzy logic models with which models are simultaneously compressed and processed in the most compact representation possible yet. The applicability of this approach is also demonstrated through experiments on a case study concerning dynamic hazards in a Fuzzy D-Flip Flop. Performance measures like runtime and memory consumptions are also provided for different scenarios. Manuscript profile

Open Access Article

12 - Web Robot Detection Using Fuzzy Rough Set Theory
S. Rahimi J. Hamidzadeh

20.1001.1.16823745.1396.15.2.17.8

Web robots are software programs that traverse the internet autonomously. Their most important task is to fetch information and send it to the origin server. The high consumption of network bandwidth by them and server performance reduction, have caused the web robot de More

Web robots are software programs that traverse the internet autonomously. Their most important task is to fetch information and send it to the origin server. The high consumption of network bandwidth by them and server performance reduction, have caused the web robot detection problem. In this paper, fuzzy rough set theory has been used for web robot detection. The proposed method includes 4 phases. In the first phase, user sessions have identified using fuzzy rough set clustering. In the second phase, a vector of 10 features is extracted for each session. In the third phase, the identified sessions are labeled using a heuristic method. In the fourth phase, these labels are improved using fuzzy rough set classification. The proposed method performance has been evaluated on a real world dataset. The experimental results have been compared with state-of-the-art methods, and show the superiority of the proposed method in terms of F-measure. Manuscript profile

Open Access Article

13 - Transmission Expansion Planning in a Deregulated Power System Using Multiobjective Differential Evolution Algorithm
f. rashidi

20.1001.1.16823745.1396.15.4.2.7

Transmission lines are widely used for transferring electrical energy from power plants to loads, interconnecting load centers and improving reliability of power systems. Due to recent society developments, the need for electrical energy has increased which in turn requ More

Transmission lines are widely used for transferring electrical energy from power plants to loads, interconnecting load centers and improving reliability of power systems. Due to recent society developments, the need for electrical energy has increased which in turn requires more investment in constructing additional electrical transmission lines. Power system restructuring and deregulation has increased uncertainties in transmission expansion planning and made investment in electrical transmission lines more complicated and less appealing for private parties. This paper proposes a new approach for transmission line expansion planning in deregulated networks. To do that, a multi objective programming problem which consists of various objective functions such as minimizing capital investment for constructing new transmission lines, minimizing congestion in transmission lines and maximizing the investment from private parties is suggested such that access to competitive, economic and reliable energy market is facilitated. To solve the proposed multi objective optimization problem, the Pareto differential evolution algorithm is used. Applying this algorithm to the proposed multi objective programming problem generates set of optimal plans that shows the best compromise between objective functions. The final plan, among the generated plans, is selected using a max-min fuzzy decision making. The proposed method is applied on the IEEE 24 bus test system and effectiveness of the proposed method is verified. Manuscript profile

Open Access Article

14 - A Cloud-based Learnable Agent-oriented Approach to Control and improve Pacemaker Operation
H. Banki نگار مجمع A. Monadjemi

20.1001.1.16823745.1396.15.4.10.5

This paper aims to present a cloud-based learning agent-oriented approach for verification of the pacemaker behavior by monitoring and heart rate adjustment of an arrhythmic patient. In case of the pacemaker failure or inappropriate heart rate generation, the patient is More

This paper aims to present a cloud-based learning agent-oriented approach for verification of the pacemaker behavior by monitoring and heart rate adjustment of an arrhythmic patient. In case of the pacemaker failure or inappropriate heart rate generation, the patient is put at risk. Using the proposed approach, one can directs the pacemaker rate to correct one when it is incorrect. Using a learnable software agent, the proposed approach is able to learn un-predefined situations and operates accordingly. The proposed approach is cloud based meaning that it sends a message through cloud in case of a critical situation. After determining the patient heart rate by pacemaker, the proposed method verifies this rate against the predefined physician suggestion and automatically corrects it based on a reinforcement learning mechanism if there is some conflict. The proposed method was implemented and installed on a tablet as a patient mobile device for monitoring the pacemaker implanted in the patient chest. The contrast between results of our approach and expected results existing in the dataset showed our approach improved the pacemaker accuracy until 13.24%. The use of the software agent with reinforcement learning is able to play a significant role in improving medical devices in case of critical situations. Manuscript profile

Open Access Article

15 - Torque Ripple Reduction Technique in SRM for Low Speed Range by Employing Fuzzy Logic for Dynamic Controlling of TSF
H. Moradi Cheshmeh-Beigi E. Nouri

20.1001.1.16823745.1397.16.2.11.9

In this paper, in order to reduce torque ripple for low speed range in non-commutation region, instead of exciting by a DC current, a adjustable current according to rotor position is injected. Also, to reduce torque ripple in commutation region the modified torque shar More

In this paper, in order to reduce torque ripple for low speed range in non-commutation region, instead of exciting by a DC current, a adjustable current according to rotor position is injected. Also, to reduce torque ripple in commutation region the modified torque sharing function (TSF) method has been used. In the proposed method, TSF is modified by a feedback from the motor speed and applying it to a fuzzy controller according to speed value. In the proposed method, motor speed, torque error, and torque error derivative are used as fuzzy controller inputs, which Turn-On and overlap angles between the phases are changed as a function of motor speed. Also reference torque of adjacent phase is modified as a function of torque error and torque error derivative. In this method, TSF is modified dynamically and momentary. The exact simulation based on Matlab/Simulink for a 3-phase 6/4 SRM are carried out to verify the effectiveness of the proposed novel method for 0 to 1500rpm speed range. Manuscript profile

Open Access Article

16 - Close Loop Identification for Combustion System by Recurrent Adaptive Neuro-Fuzzy Inference System and Network with Exogenous Inputs
E. Aghadavoodi G. Shahgholian

20.1001.1.16823745.1397.16.3.7.7

Boiler-turbine is a multi-variable and complicated system in steam power plants including combustion, temperature and drum water level. Selecting control loops as a unique loop in order to identify and control the boiler as a whole unit is a difficult and complicated ta More

Boiler-turbine is a multi-variable and complicated system in steam power plants including combustion, temperature and drum water level. Selecting control loops as a unique loop in order to identify and control the boiler as a whole unit is a difficult and complicated task, because of nonlinear time variant dynamic characteristics of the boiler. It is necessary to identify each control group in order to accomplish a realistic and effective model, appropriate for designing an efficient controller. Both the effective and efficient performance of the identified model during the load change is of major importance. Here, not all parts of the system should be considered as a unit part, if determining and effective and realistic model is sought. The combustion loop of the 320 MW steam power plant of Islam Abad, Isfahan is the subject. Due to the sensitivity and complexity of the system, with respect to its nonlinear and closed loop characteristics, the identification of the system is conducted through intelligent procedures like recurrent adaptive neuro-fuzzy inference system (RANFIS) and nonlinear autoregressive model with exogenous input (NARX). The comparisons of the findings with actual data collected from the plant are presented and the accuracy of the procedures is determined. Manuscript profile

Open Access Article

17 - Priority-Based Task Scheduling Using Fuzzy System in Mobile Edge Computing
Entesar Hosseini Mohsen Nickray SH. GH.

20.1001.1.16823745.1401.20.4.1.1

Mobile edge computing (MEC) are new issues to improve latency, capacity and available resources in Mobile cloud computing (MCC). Mobile resources, including battery and CPU, have limited capacity. So enabling computation-intensive and latency-critical applications are i More

Mobile edge computing (MEC) are new issues to improve latency, capacity and available resources in Mobile cloud computing (MCC). Mobile resources, including battery and CPU, have limited capacity. So enabling computation-intensive and latency-critical applications are important issue in MEC. In this paper, we use a standard three-level system model of mobile devices, edge and cloud, and propose two offloading and scheduling algorithms. A decision-making algorithm for offloading tasks is based on the greedy Knapsack offloading algorithm (GKOA) on the mobile device side, which selects tasks with high power consumption for offloading and it saves energy consumption of the device. On the MEC side, we also present a dynamic scheduling algorithm with fuzzy-based priority task scheduling (FPTS) for prioritizing and scheduling tasks based on two criteria. Numerical results show that our proposed work compared to other methods and reduces the waiting time, latency and system overhead. Also, provides the balance of the system with the least number of resources. And the proposed system reduces battery consumption in the smart device by up to 90%. The results show that more than 92% of tasks are executed successfully in the edge environment. Manuscript profile

Open Access Article

18 - Robust Optimal Stable Fuzzy Controller Design for Stabilization of Electric Vehicle Speed, in Presence of Parametric Uncertainties and External Disturbances
Mohammad Veysi M. Shasadeghi M. R. Soltanpour

20.1001.1.16823745.1398.17.2.1.6

In electric vehicle’s nonlinear dynamic equations, some parameters has uncertainty such as the coefficient of rolling resistance, drag coefficient, armature resistance and field winding resistance. Design of a controller that is robust in the presence of these parametri More

In electric vehicle’s nonlinear dynamic equations, some parameters has uncertainty such as the coefficient of rolling resistance, drag coefficient, armature resistance and field winding resistance. Design of a controller that is robust in the presence of these parametric uncertainties and also in presence of external disturbances, and on the other hand simultaneously satisfies the optimality criteria, is a challenging issue. In practical applications, in addition to the above problem, the computational load of the control input should also be considered and provide a rational interaction between the controller's desirable performance and the calculations volume. In the present paper, a robust optimal stable fuzzy controller based on the parallel distributed compensation is designed, using Takagi-Sugeno fuzzy model of electric vehicle. The stabilizer feedback gains of fuzzy model, the upper bound of the uncertainties, the upper bound of the disturbances effect, and the upper bound of the cost function are obtained completely offline, through the solving of a minimization problem based on the linear matrix inequality. Therefore, the calculation volume of the control input is extremely low. This allows the practical implementation of the proposed controller. The favorable performance of the proposed controller is demonstrated in five-step simulations. Manuscript profile

Open Access Article

19 - Space Time Code Design in Phased-MIMO Radars to Achieve High Resolution in Board and Velocity
Roholah Vahdani Hossein Khaleghi Mohsen Fallah Joshaghani

20.1001.1.16823745.1400.19.4.2.5

In this paper, space-time codes for MIMO radar are used to achieve high resolution at target range and speed. The two-dimensional ambiguity function is known as a tool to compare radar performance in terms of resolution. A space-time code can be designed based on minimi More

In this paper, space-time codes for MIMO radar are used to achieve high resolution at target range and speed. The two-dimensional ambiguity function is known as a tool to compare radar performance in terms of resolution. A space-time code can be designed based on minimizing the distance between the actual target parameters in range, speed and angle. For this purpose, high resolution can be achieved by analyzing the ambiguity function and narrowing it down as much as possible. In this paper, the ambiguity function with two variables of amplitude and speed mismatch is considered and a new criterion to achieve high resolution performance in this field is proposed. In this case, by optimizing the proposed cost functions, the optimal space-time code is extracted. The proposed design can also be extended to phased-MIMO radar. The simulation results also show that our proposed scheme has a very narrow ambiguity function around the origin, while it has a performance very close to the optimal state in terms of target identification probability. Manuscript profile

Open Access Article

20 - An Efficient Approach to Reduce Energy Consumption in Internet of Things Routing
M. Asgari M. Fathy Mohammad Shahverdy M. Soheili Nayer

20.1001.1.16823745.1398.17.3.14.1

The Internet of Things (IOT) is a new concept in the area of monitoring information transmission and remote control of things, existents and equipment that has been able to adapt itself with different industries and substructures easily. The information transmission wit More

The Internet of Things (IOT) is a new concept in the area of monitoring information transmission and remote control of things, existents and equipment that has been able to adapt itself with different industries and substructures easily. The information transmission with regard to the non-homogenous environment of internet of things has been a challengeable topic and use of routing methods by considering the limitations of processing, calculating, saving and communicating has been known as a necessary issue. Various algorithms with special applications have been already introduced in the domain of internet of things and wireless sensor networks that each one somehow has been successful in achieving the routing goals. Some proposed protocols in this field have used a tree structure for gathering the network information. These methods in selecting the parent or children of graph are affected by important challenges depending on the type of application. In this paper, at first, a general classification of advantages and defects of recent methods has been done in the domain of routing the internet of things and then a routing method service quality awareness in routing based on fuzzy system has been suggested. The results of simulations and assessment express that the suggested method in the tests of energy productivity, delay ratio and data delivery ratio have better performance than the recent methods. Manuscript profile

Open Access Article

21 - Feature Selection and Cancer Classification Based on Microarray Data Using Multi-Objective Cuckoo Search Algorithm
kh. Kamari f. rashidi a. Khalili

20.1001.1.16823745.1398.17.4.9.8

Microarray datasets have an important role in identification and classification of the cancer tissues. In cancer researches, having a few samples of microarrays in cancer researches is one of the most concerns which lead to some problems in designing the classifiers. Mo More

Microarray datasets have an important role in identification and classification of the cancer tissues. In cancer researches, having a few samples of microarrays in cancer researches is one of the most concerns which lead to some problems in designing the classifiers. Moreover, due to the large number of features in microarrays, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Hence, appropriate gene selection method can significantly improve the performance of cancer classification. In this paper, a modified multi-objective cuckoo search algorithm is used to feature selection and sample selection to find the best available solutions. For accelerating the optimization process and preventing local optimum trapping, new heuristic approaches are included to the original algorithm. The proposed algorithm is applied on six cancer datasets and its results are compared with other existing methods. The results show that the proposed method has higher accuracy and validity in comparison to other existing approaches and is able to select the small subset of informative genes in order to increase the classification accuracy. Manuscript profile

Open Access Article

22 - Propose a Proper Algorithm for Incremental Learning Based on Fuzzy Least Square Twin Support Vector Machines
Javad Salimi Sartakhti Salman Goli

20.1001.1.16823745.1400.19.3.3.4

Support Vector machine is one of the most popular and efficient algorithms in machine learning. There are several versions of this algorithm, the latest of which is the fuzzy least squares twin support vector machines. On the other hand, in many machine learning applica More

Support Vector machine is one of the most popular and efficient algorithms in machine learning. There are several versions of this algorithm, the latest of which is the fuzzy least squares twin support vector machines. On the other hand, in many machine learning applications input data is continuously generated, which has made many traditional algorithms inefficient to deal with them. In this paper, for the first time, an incremental version of the fuzzy least squares twin support vector algorithm is presented. The proposed algorithmis represented in both online and quasi-online modes. To evaluate the accuracy and precision of the proposed algorithmfirst we run our algorithm on 6 datasets of the UCI repository. Results showthe proposed algorithm is more efficient than other algorithms (even non-incremental versions). In the second phase in the experiments, we consider an application of Internet of Things, and in particular in data related to daily activities which inherently are incremental. According to experimental results, the proposed algorithm has the best performance compared to other incremental algorithms. Manuscript profile

Open Access Article

23 - Improving Energy Consumption in Wireless Sensor Networks Using Shuffled Frog Leaping Algorithm and Fuzzy Logic
Shayesteh Tabatabaey

20.1001.1.16823745.1400.19.1.11.8

Wireless sensor networks consist of thousands of sensor nodes with limited energy. Energy efficiency is a fundamental challenge issue for wireless sensor networks. Clustering sensor nodes in separate categories and exchanging information through clusters is one of the w More

Wireless sensor networks consist of thousands of sensor nodes with limited energy. Energy efficiency is a fundamental challenge issue for wireless sensor networks. Clustering sensor nodes in separate categories and exchanging information through clusters is one of the ways to improve energy consumption. This paper presents a new cluster-based routing protocol called SFLCFBA. The proposed protocol biologically uses fast and effective search features inspired by the Shuffled Frog Leaping algorithm, which acts based on the Frog food behavior to cluster sensor nodes. The proposed protocol also uses fuzzy logic to calculate the node fitness, based on the two criteria of distance to the sink and the remaining energy of the sensor node or power of battery level. IEEE 802.15.4 Protocol and NODIC Protocol with the proposed methodology and OPNET Simulator were simulation and the results in terms of energy consumption, end to end delay, signal to noise ratio, the success property data and throughput were compared with each other. The results of the simulation showed that the proposed method outperforms the IEEE 802.15.4 Protocol and NODIC Protocol due to the use of the criteria listed. Manuscript profile

Open Access Article

24 - A Hybrid Long-Term Probabilistic Net Load Forecasting Approach Considering Renewable Energies Power in Smart Grids
Mohsen Jahantigh majid moazzami

20.1001.1.16823745.1400.19.2.2.1

With the growth and integration of distributed generation resources in smart grids, net load forecasting is of significant importance. A hybrid optimization method is proposed in this paper for probabilistic net load forecasting using neighborhood component analysis and More

With the growth and integration of distributed generation resources in smart grids, net load forecasting is of significant importance. A hybrid optimization method is proposed in this paper for probabilistic net load forecasting using neighborhood component analysis and solving regression problem with the aid of mini-batch LBFGS method. Net load forecasting is suggested in this paper trough forecast combination via adaptive network-based fuzzy inference system. The structure includes a combination of several long-term forecasts, including forecasts of load, the generation of a solar station, and the generation of a wind farm with wind turbines equipped with doubly-fed induction generator. Also, the net load forecasting and the relationship between errors of load, wind and solar predictions are studied in this paper. The simulation results of the proposed method and its comparison with Tao and quantile regression models show that mean absolute percentage error of load forecasting, and the forecasts of solar and wind generations improved by 0.947%, 0.3079% and 0042%, respectively which result to a decrease in net load forecasting error. Manuscript profile

Open Access Article

25 - Design and Implementation of Fuzzy Sliding Mode Controller for Motion Control of an Electric Shake Table Using Adaptive Extended Kalman Filter
Nima rajabi Ramazan Havangi

20.1001.1.16823745.1400.19.2.6.5

In this paper, Design of a fuzzy sliding mode controller (FSMC) with adaptive extended Kalman filter (AEKF) for controlling a shake table system with electric actuator and ball-screw mechanism. Due to the uncertainties regarding the model parameters and the noise of the More

In this paper, Design of a fuzzy sliding mode controller (FSMC) with adaptive extended Kalman filter (AEKF) for controlling a shake table system with electric actuator and ball-screw mechanism. Due to the uncertainties regarding the model parameters and the noise of the data of the two encoder and accelerometer sensors, there are many problems in controlling this system. Therefore, it is crucial to employ a non-precise model-based controller and a nonlinear adaptive filter. The fuzzy sliding mode control and Extended Kalman filter are a good way to control this system. In sliding mode control, chattering at the control input is inevitable. In this paper, a simple fuzzy inference mechanism is used to reduce the undesirable phenomenon of chattering by correctly estimating the upper bound of uncertainty. In the following, a recursive method is used to determine the system and measurement noise covariance matrices. The data of the two encoder and accelerometer sensors are combined in the adaptive extended Kalman filter and the results in noise elimination and parameter estimation are investigated. Linear speed feedback available through the Kalman filter is used to stabilize and control the closed loop system. The end is examined to check the performance of the control structure provided by the seismic table test. The results show that the proposed method is very effective. Manuscript profile

Open Access Article

26 - Detector Design & Power Allocation of Frequency Diverse Phased Multi Input Multi Output Radar within Nonhomogeneous Environments
Hamid Reza Fotoohi Firouzabad Seyed Mehdi Hosseini Andargoli Hossein Ghanei Yakhdan J. Abouei

In recent years, Phased-Multiple-Input, Multiple-Output radars (PMRs) have attracted great interest. PMR can combine the advantages of both MIMO radar and phased array radar. Here, PMR transmits orthogonal signals from all subarrays to provide both waveform frequency di More

In recent years, Phased-Multiple-Input, Multiple-Output radars (PMRs) have attracted great interest. PMR can combine the advantages of both MIMO radar and phased array radar. Here, PMR transmits orthogonal signals from all subarrays to provide both waveform frequency diversity and high coherent processing gain. In this paper dealt with detector design in the presence of heterogeneous clutter based on the unknown scattering coefficients for PMR. Then, detection probability and false-alarm probability are computed based on the derived optimum detector. At the end, the power allocation problem is investigated analytically. The numerical simulations show that obtained optimal detector is joint spatial-temporal filter, which, the clutters are effectively weakened in PMR. Furthermore, simulation results illustrate that proposed power allocation algorithm improve detection performance of PMR in comparison with PR and equal power PMR. Manuscript profile

Open Access Article

27 - Neural-Fuzzy Network and Z-Source Converter Adaptive Controller for Control the Power of the Hybrid Network Consisting of Doubly-Fed Induction Generator and Solar Cel
ali akbar habibi borzou yousefi abdolreza noori shirazi Mohammad rezvani

20.1001.1.16823745.1401.20.3.11.9

Renewable energies outfitted with low latency assets as wind turbines and photovoltaic exhibits give significant adverse consequences through power framework dynamic protections. For this issue, in view of fostering a high voltage direct current (HVDC) interface, a vers More

Renewable energies outfitted with low latency assets as wind turbines and photovoltaic exhibits give significant adverse consequences through power framework dynamic protections. For this issue, in view of fostering a high voltage direct current (HVDC) interface, a versatile Neuro-Fuzzy-based damping regulator is introduced in this paper for working on unique execution of low inertia resources associated with power frameworks. The created power framework comprises of various age sources including seaward and inland wind turbines (WTs), photovoltaic exhibits (PVs) and limited doubly fed induction generators (DFIGs) which are incorporated together through an interconnected framework. For this situation, thinking about various functional and innovative conditions, damping execution of proposed ANFIS plot is assessed. The proposed plot is a non-model-based regulator which utilizes the benefits of both neural and fluffy rationales together for giving a quick and secure design of damping regulator through continuous recreations. To research ANFIS plot through genuine cases, considering a commonplace microgrid comprises of various low-latency assets (for example WT, PV, DFIG), the framework damping exhibitions through hamper occasions are assessed. Recreation results demonstrate viability and effectiveness of the proposed plot for damping dynamic motions of low inertia resources with high damping proportions with respect to extreme issue occasions. Manuscript profile

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