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.

Intelligent Optimization of Sliding-Mode Fuzzy Logic Controllers

Abstract: Optimization is the selection process of the best elements with respect to some criterion from a feasible set of variables. There may be single or multiple objectives to be considered during optimization. The optimization process generally involves the minimization of a cost or maximization of a profit. Sliding mode controller design problem ends up with the selection of values for its parameters which may include trials and errors. In this chapter, using intelligent optimization approaches, the parameters of sliding mode controllers are optimized to minimize the effect of chattering while the tracking error is minimized. Since these two objective functions are conflicting objective functions, it is required to use multiobjective intelligent optimization approaches.

Estimation of the parameters of wavelet neural networks using simultaneous use of genetic algorithm and recursive least square

Abstract: In this paper, a novel identification scheme based on wavelet neural network structure is proposed. The objective function for identification considered in this paper is the sum of squared error. In order to optimize this objective, the genetic algorithm (GA) which is a global optimization is used for the parameters which appear nonlinearly in the wavelet structure. Recursive least square algorithm is used for the parameters which appear linearly in the output of wavelet neural network because it is known to be an optimal estimator for these parameters. The proposed training algorithm is used to identify chaotic system and a highly nonlinear dynamical system. Simulation results show that the proposed method identifies input/output data with higher performance in terms of sum of squared error when it is compared to gradient descent method.

Ensemble of Deep Belief Network and Bayesian Adaptive Aggregation for Regression

Abstract: Ensemble modeling of Neural Networks is a strategy where multiple alternative models (ensemble members) are constructed and then their forecasts are ensembled using various combination approaches. Ensemble of Neural Networks has proved the concept behind this strategy. Deep neural network offers potential opportunities to overcome traditional ensemble of neural networks. This paper proposes an deep belief networks ensemble (DBN). The ensemble members of DBN are constructed with different number of epochs Which results in superior generalization ability. The outputs of these DBNs are aggregated by a Bayesian model averaging method. The proposed Bayesian adopted ensemble of DBNs is evaluated on two benchmark data sets. Comparison of the proposed model is evaluated with simple averaging and single DBN over a number of forecasting measuring that shows better performance of the proposed model.

Guaranteed cost adaptive sliding mode fuzzy control systems

Abstract: In this paper, a novel sliding mode control technique is proposed which benefits from recursive least square adaptation laws. Since this method has high resistance against uncertainty and may result in a desirable transient response, this method is one of the most commonly used nonlinear control methods. In this method, the uncertain dynamics of system is stabilized by applying a discontinuous control signal. The principle of this type of controller is to force the states of the system towards sliding manifold and maintain the states of this stable manifold which defines the desired behavior of the system. It is possible to model dynamic behavior of physical systems in terms of fuzzy systems. In order to tune the parameters of fuzzy system, a cost function based on sliding mode is proposed. The solution to this cost function results in optimal adaptation laws whose stability analysis is done using an appropriate Lyapunov function. The proposed adaptive sliding mode fuzzy controller is simulated on an inverted pendulum to test its efficacy and performance in control of a benchmark system.

Case Studies: Identification Examples

Abstract: In this chapter, the learning algorithms proposed in the previous chapters (GD-based, SMC theory-based, EKF and hybrid PSO-based learning algorithms) are used to identify and predict two nonlinear systems, namely Mackey-Glass and a second-order nonlinear time-varying plant. Several comparisons are made, and it has been shown that the proposed SMC theory-based algorithm has faster convergence than existing methods such as GD-based and swarm intelligence-based methods. Moreover, the proposed learning algorithm has an explicit form, and it is easier to implement than other existing methods. However, for offline algorithms for which computation time is not an issue, the hybrid training method based on PSO and SMC theory may be a preferable choice.

Adaptive Control

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.

Particle swarm optimisation for feature selection in classification

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.