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Mojtaba Ahmadieh Khanesar

Bio: Mojtaba Ahmadieh Khanesar is an academic researcher from University of Nottingham. The author has contributed to research in topics: Fuzzy logic & Fuzzy control system. The author has an hindex of 23, co-authored 96 publications receiving 1695 citations. Previous affiliations of Mojtaba Ahmadieh Khanesar include K.N.Toosi University of Technology & Semnan University.


Papers
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Journal ArticleDOI
01 Dec 2018
TL;DR: Under PSCs performances of the proposed MFA, firefly algorithm (FA), PSO and FA methods in tracking the global MPP are very satisfactory and the proposed method has a higher tracking speed than FA and PSO methods under partial shading conditions.
Abstract: A photovoltaic (PV) system under partial shading condition (PSC) may experience several local maximum power points (MPP). Classical maximum power point tracking (MPPT) techniques, developed for uniform solar radiation on PV arrays, are incapable of discriminating between global and local maximum power points. In this paper, a modified firefly algorithm (MFA) is used and investigated with the objective of PV system MPP tracking under PSCs. A comprehensive evaluation among the proposed MFA, firefly algorithm (FA) particle swarm optimization (PSO), and perturbation and observation (P&O) method, as one of the classical methods of MPPT in uniform irradiance, is performed. Performances of the mentioned methods are studied under various PSCs in MATLAB/Simulink software environment. The obtained results show that under PSCs performances of the proposed method, PSO and FA methods in tracking the global MPP are very satisfactory. Furthermore, the proposed method has a higher tracking speed than FA and PSO methods under partial shading conditions.

24 citations

Journal ArticleDOI
01 Jul 2012
TL;DR: A robust indirect model reference fuzzy control scheme for control and synchronization of chaotic nonlinear systems subject to uncertainties and external disturbances and it is shown that by the use of an appropriate reference signal, it is possible to make the reference model follow the master chaotic system.
Abstract: This paper presents a robust indirect model reference fuzzy control scheme for control and synchronization of chaotic nonlinear systems subject to uncertainties and external disturbances. The chaotic system with disturbance is modeled as a Takagi–Sugeno fuzzy system. Using a Lyapunov function, stable adaptation laws for the estimation of the parameters of the Takagi–Sugeno fuzzy model are derived as well as what the control signal should be to compensate for the uncertainties. The synchronization of chaotic systems is also considered in the paper. It is shown that by the use of an appropriate reference signal, it is possible to make the reference model follow the master chaotic system. Then, using the proposed model reference fuzzy controller, it is possible to force the slave to act as the reference system. In this way, the chaotic master and the slave systems are synchronized. It is shown that not only can the initial values of the master and the slave be different, but also there can be parametric differences between them. The proposed control scheme is simulated on the control and the synchronization of Duffing oscillators and Genesio–Tesi systems.

22 citations

Book ChapterDOI
01 Jan 2016
TL;DR: In this chapter, type-1 and type-2 TSK fuzzy logic models are introduced, instead of using fuzzy sets in the consequent part (as in Mamdani models), the TSK model uses a function of the input variables.
Abstract: The two most common artificial intelligence techniques, FLSs and ANNs, can be used in the same structure simultaneously, namely as “fuzzy neural networks.” The advantages of ANNs such as learning capability from input-output data, generalization capability, and robustness and the advantages of fuzzy logic theory such as using expert knowledge are harmonized in FNNs. In this chapter, type-1 and type-2 TSK fuzzy logic models are introduced. Instead of using fuzzy sets in the consequent part (as in Mamdani models), the TSK model uses a function of the input variables. The order of the function determines the order of the model, e.g., zeroth-order TSK model, first-order TSK model, etc.

22 citations

Journal ArticleDOI
TL;DR: It is shown that the proposed fractional order controller can be implemented in a low cost embedded system and can successfully control a highly nonlinear dynamic system.

21 citations

Book ChapterDOI
01 Jan 2016
TL;DR: This chapter introduces the concepts of type-1 fuzzy sets and T1FLCs, a super set of conventional Boolean logic that can handle concepts the authors commonly face in daily life, like very old, old, young and very young.
Abstract: While Boolean logic results are restricted to 0 and 1, fuzzy logic results are between 0 and 1. In other words, fuzzy logic, as a super set of conventional Boolean logic, defines some intermediate values between sharp evaluations like absolute true and absolute false. That means fuzzy sets can handle concepts we commonly face in daily life, like very old , old , young and very young . In this chapter, we introduce the concepts of type-1 fuzzy sets and T1FLCs.

18 citations


Cited by
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Journal Article
TL;DR: In this paper, two major figures in adaptive control provide a wealth of material for researchers, practitioners, and students to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs.
Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

1,814 citations

01 Jan 1996

1,282 citations

Journal ArticleDOI
TL;DR: Results prove the capability of the proposed binary version of grey wolf optimization (bGWO) to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.

958 citations

Journal ArticleDOI
01 May 2014
TL;DR: Experiments on twenty benchmark datasets show that PSO with the new initialisation strategies and/or the new updating mechanisms can automatically evolve a feature subset with a smaller number of features and higher classification performance than using all features.
Abstract: In classification, feature selection is an important data pre-processing technique, but it is a difficult problem due mainly to the large search space. Particle swarm optimisation (PSO) is an efficient evolutionary computation technique. However, the traditional personal best and global best updating mechanism in PSO limits its performance for feature selection and the potential of PSO for feature selection has not been fully investigated. This paper proposes three new initialisation strategies and three new personal best and global best updating mechanisms in PSO to develop novel feature selection approaches with the goals of maximising the classification performance, minimising the number of features and reducing the computational time. The proposed initialisation strategies and updating mechanisms are compared with the traditional initialisation and the traditional updating mechanism. Meanwhile, the most promising initialisation strategy and updating mechanism are combined to form a new approach (PSO(4-2)) to address feature selection problems and it is compared with two traditional feature selection methods and two PSO based methods. Experiments on twenty benchmark datasets show that PSO with the new initialisation strategies and/or the new updating mechanisms can automatically evolve a feature subset with a smaller number of features and higher classification performance than using all features. PSO(4-2) outperforms the two traditional methods and two PSO based algorithm in terms of the computational time, the number of features and the classification performance. The superior performance of this algorithm is due mainly to both the proposed initialisation strategy, which aims to take the advantages of both the forward selection and backward selection to decrease the number of features and the computational time, and the new updating mechanism, which can overcome the limitations of traditional updating mechanisms by taking the number of features into account, which reduces the number of features and the computational time.

457 citations

Journal ArticleDOI
TL;DR: In this survey, fourteen new and outstanding metaheuristics that have been introduced for the last twenty years other than the classical ones such as genetic, particle swarm, and tabu search are distinguished.

450 citations