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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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Proceedings ArticleDOI
27 Jun 2007
TL;DR: This algorithm is shown to be a better interpretation of continuous PSO into discrete PSO than the older versions and a number of benchmark optimization problems are solved using this concept and quite satisfactory results are obtained.
Abstract: Particle swarm optimization (PSO) as a novel computational intelligence technique, has succeeded in many continuous problems. But in discrete or binary version there are still some difficulties. In this paper a novel binary PSO is proposed. This algorithm proposes a new definition for the velocity vector of binary PSO. It will be shown that this algorithm is a better interpretation of continuous PSO into discrete PSO than the older versions. Also a number of benchmark optimization problems are solved using this concept and quite satisfactory results are obtained.

355 citations

Journal ArticleDOI
TL;DR: Simulation results show that the proposed fuzzy sliding mode controller is capable of controlling nonlinear dynamical systems over a network, which is subject to bounded external disturbances, time-varying network-induced delays, and packet losses with adequate performance.
Abstract: Two major challenges in networked control systems are the time-varying networked-induced delays and the packet losses. To alleviate these problems, this study presents a novel fuzzy sliding mode controller, where a fuzzy system is used to estimate the nonlinear dynamical system online, and the networked-induced delay is handled by Pade approximation. The problem of packet losses is handled by viewing them as large time-varying delays in the system. The sliding mode-based design procedure used ensures the stability and the robustness of the proposed controller in the presence of disturbances and time-varying networked-induced time delays. Using an appropriate Lyapunov function, it is proved that the tracking error converges to the neighborhood of zero asymptotically. Furthermore, since the adaptation laws of the parameters are derived by using of the Lyapunov function, these laws are also found to be stable. Simulation results show that the proposed fuzzy sliding mode controller is capable of controlling nonlinear dynamical systems over a network, which is subject to bounded external disturbances, time-varying network-induced delays, and packet losses with adequate performance.

137 citations

Journal ArticleDOI
TL;DR: The simulation results show that the proposed novel type-2 fuzzy membership function with the extended Kalman filter has noise rejection property, which is faster and more efficient than the particle swarm optimization method.
Abstract: In this paper, the use of extended Kalman filter for the optimization of the parameters of type-2 fuzzy logic systems is proposed. The type-2 fuzzy logic system considered in this study benefits from a novel type-2 fuzzy membership function which has certain values on both ends of the support and the kernel, and uncertain values on other parts of the support. To have a comparison of the extended Kalman filter with other existing methods in the literature, particle swarm optimization and gradient descent-based methods are used. The proposed type-2 fuzzy neuro structure is tested on different noisy input-output data sets, and it is shown that extended Kalman filter has a better performance as compared to the gradient descent-based methods. Although the performance of the proposed method is comparable with the particle swarm optimization method, it is faster and more efficient than the particle swarm optimization method. Moreover, the simulation results show that the proposed novel type-2 fuzzy membership function with the extended Kalman filter has noise rejection property. Kalman filter is also used to train the parameters of type-2 fuzzy logic system in a feedback error learning scheme. Then, it is used to control a real-time laboratory setup ABS and satisfactory results are obtained.

132 citations

Journal ArticleDOI
01 Oct 2011
TL;DR: The proposed type-2 fuzzy neuro structure is tested on different input-output data sets, and it is shown that the T2FLS with the proposed novel membership function has better noise reduction property when compared to the type-1 counterparts.
Abstract: In this paper, the noise reduction property of type-2 fuzzy logic (FL) systems (FLSs) (T2FLSs) that use a novel type-2 fuzzy membership function is studied. The proposed type-2 membership function has certain values on both ends of the support and the kernel and some uncertain values for the other values of the support. The parameter tuning rules of a T2FLS that uses such a membership function are derived using the gradient descend learning algorithm. There exist a number of papers in the literature that claim that the performance of T2FLSs is better than type-1 FLSs under noisy conditions, and the claim is tried to be justified by simulation studies only for some specific systems. In this paper, a simpler T2FLS is considered with the novel membership function proposed in which the effect of input noise in the rule base is shown numerically in a general way. The proposed type-2 fuzzy neuro structure is tested on different input-output data sets, and it is shown that the T2FLS with the proposed novel membership function has better noise reduction property when compared to the type-1 counterparts.

87 citations

Journal ArticleDOI
TL;DR: The developed algorithm applies fully-sliding-mode parameter update rules for both the premise and consequent parts of type-2 FNNs, and it has been realized and shown that the proposed algorithm has faster convergence speed than the existing methods such as gradient-based and swarm-intelligence-based methods.
Abstract: In order to achieve faster and more robust convergence (particularly under noisy working environments), a sliding-mode-theory-based learning algorithm has been proposed to tune both the premise and consequent parts of type-2 fuzzy neural networks (FNNs) in this paper. Different from recent studies, where sliding-mode-control-theory-based rules are proposed for only the consequent part of the network, the developed algorithm applies fully-sliding-mode parameter update rules for both the premise and consequent parts of type-2 FNNs. In addition, the responsible parameter for sharing the contributions of the lower and upper parts of the type-2 fuzzy membership functions is also tuned. Moreover, the learning rate of the network is updated during the online training. The stability of the proposed learning algorithm has been proved by using an appropriate Lyapunov function. Several comparisons have been realized and shown that the proposed algorithm has faster convergence speed than the existing methods such as gradient-based and swarm-intelligence-based methods. Moreover, the proposed learning algorithm has a closed form, and it is easier to implement than the other existing methods.

86 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