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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: Sliding mode control of rotary inverted pendulum is presented and based on this performance two sliding surfaces are designed, then system is controlled by proper definition of a lyapunov function.
Abstract: This paper presents sliding mode control of rotary inverted pendulum. Rotary inverted pendulum is a nonlinear, unstable and non-minimum-phase system. Designing sliding mode controller for such system is difficult in general. Here, first the desired performance is introduced and based on this performance two sliding surfaces are designed, then system is controlled by proper definition of a lyapunov function. The lyapunov function designed puts more emphasis on the control of the inverted pendulum rather than the control of the motor.

28 citations

Book ChapterDOI
01 Jan 2009
TL;DR: A modified discrete particle swarm optimization (PSO) is successfully used based technique for generating optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system while satisfying system load demand and crew constraints.
Abstract: Particle swarm optimization (PSO) was originally designed and introduced by Eberhart and Kennedy (Ebarhart, Kennedy, 1995; Kennedy, Eberhart, 1995; Ebarhart, Kennedy, 2001). The PSO is a population based search algorithm based on the simulation of the social behavior of birds, bees or a school of fishes. This algorithm originally intends to graphically simulate the graceful and unpredictable choreography of a bird folk. Each individual within the swarm is represented by a vector in multidimensional search space. This vector has also one assigned vector which determines the next movement of the particle and is called the velocity vector. The PSO algorithm also determines how to update the velocity of a particle. Each particle updates its velocity based on current velocity and the best position it has explored so far; and also based on the global best position explored by swarm (Engelbrecht, 2005; Sadri, Ching, 2006; Engelbrecht, 2002). The PSO process then is iterated a fixed number of times or until a minimum error based on desired performance index is achieved. It has been shown that this simple model can deal with difficult optimization problems efficiently. The PSO was originally developed for realvalued spaces but many problems are, however, defined for discrete valued spaces where the domain of the variables is finite. Classical examples of such problems are: integer programming, scheduling and routing (Engelbrecht, 2005). In 1997, Kennedy and Eberhart introduced a discrete binary version of PSO for discrete optimization problems (Kennedy, Eberhart, 1997). In binary PSO, each particle represents its position in binary values which are 0 or 1. Each particle's value can then be changed (or better say mutate) from one to zero or vice versa. In binary PSO the velocity of a particle defined as the probability that a particle might change its state to one. This algorithm will be discussed in more detail in next sections. Upon introduction of this new algorithm, it was used in number of engineering applications. Using binary PSO, Wang and Xiang (Wang & Xiang, 2007) proposed a high quality splitting criterion for codebooks of tree-structured vector quantizers (TSVQ). Using binary PSO, they reduced the computation time too. Binary PSO is used to train the structure of a Bayesian network (Chen et al., 2007). A modified discrete particle swarm optimization (PSO) is successfully used based technique for generating optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system while satisfying system load demand and crew constraints (Yare & Venayagamoorthy, 2007). Choosing optimum input subset for SVM (Zhang & Huo, 2005),

27 citations

Proceedings ArticleDOI
11 Apr 2011
TL;DR: A new training approach based on the Levenberg-Marquardt algorithm is proposed for type-2 fuzzy neural networks that results in faster training but also in a better forecasting accuracy.
Abstract: A new training approach based on the Levenberg-Marquardt algorithm is proposed for type-2 fuzzy neural networks. While conventional gradient descent algorithms use only the first order derivative, the proposed algorithm used in this paper benefits from the first and the second order derivatives which makes the training procedure faster. Besides, this approach is more robust than the other techniques that use the second order derivatives, e.g. Gauss-Newton's method. The training algorithm proposed is tested on the training of a type-2 fuzzy neural network used for the prediction of a chaotic Mackey-Glass time series. The results show that the learning algorithm proposed not only results in faster training but also in a better forecasting accuracy.

26 citations

Journal ArticleDOI
TL;DR: The results of this study believe will open the doors to elliptic MFs’ wider use of real-world identification and control applications as the proposed MF is easy to interpret in addition to its unique features.

26 citations

Proceedings ArticleDOI
12 Dec 2007
TL;DR: In this study, a fuzzy system is added to the classical sliding mode controller to adaptively estimate the equivalent signal of the sliding mode controllers and the stability of the controller and the adaptation law is guaranteed.
Abstract: In this study, a fuzzy sliding mode controller for a rotary inverted pendulum is designed. Sliding mode controllers are high performance nonlinear controllers. Not only Sliding mode controller stabilizes the system under control effectively but also it robustly compensates the effect of bounded uncertainties and shows invariance properties in the presence of bounded disturbance. In this study, a fuzzy system is added to the classical sliding mode controller to adaptively estimate the equivalent signal of the sliding mode controller. In addition, using Lyapunov theory the stability of the controller and the adaptation law is guaranteed.

26 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