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.

Adaptive Control

Probability and Measure

Binary grey wolf optimization approaches for feature selection

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.

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Particle swarm optimisation for feature selection in classification

A survey on new generation metaheuristic algorithms

This paper proposes a novel, observer-based, indirect model reference fuzzy control approach for nonlinear systems, expressed in the form of a Takagi Sugeno (TS) fuzzy model. Based on this model, an adaptive observer based, indirect model reference fuzzy controller is developed to deal with external disturbances. In contrast to what is seen in the literature on adaptive observer based TS fuzzy control systems, the proposed method is robust in the existence of bounded external disturbances and it is capable of tracking a reference signal rather than just regulation. The proposed method is simulated on the control of Chua's circuit and it is shown that it is capable of controlling this chaotic system with high performance.

Observer-based indirect model reference fuzzy control system with application to control of chaotic systems

Recurrent Interval Type-2 Fuzzy Control of 2-DOF Helicopter With Finite Time Training Algorithm

In this paper, our aim is to compare and contrast various ways of modeling uncertainty by using different type-2 fuzzy membership functions available in literature. In particular we focus on a novel type-2 fuzzy membership function, — “Elliptic membership function”. After briefly explaining the motivation behind the suggestion of the elliptic membership function, we analyse the uncertainty distribution along its support, and we compare its uncertainty modeling capability with the existing membership functions. We also show how the elliptic membership functions perform in fuzzy arithmetic. In addition to its extra advantages over the existing type-2 fuzzy membership functions such as having decoupled parameters for its support and width, this novel membership function has some similar features to the Gaussian and triangular membership functions in addition and multiplication operations. Finally, we have tested the prediction capability of elliptic membership functions using interval type-2 fuzzy logic systems on US Dollar/Euro exchange rate prediction problem. Throughout the simulation studies, an extreme learning machine is used to train the interval type-2 fuzzy logic system. The prediction results show that, in addition to their various advantages mentioned above, elliptic membership functions have comparable prediction results when compared to Gaussian and triangular membership functions.

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Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction

In this paper, the integration of Type-2 fuzzy set theory and recurrent wavelet neural network(WNN) is proposed to allow managing of non-uniform uncertainties for identifying non-linear dynamic system. The proposed Type-2 fuzzy WNN is inherently a recurrent multilayered network which constructed based on a set of Type-2 fuzzy rules and recurrent connections in the second layer of the FWNN. Each rule comprises a wavelet function in the consequent part. The structure has both advantages of recurrent and wavelet neural network which expand the basic ability of fuzzy neural network to deal with temporal problems. Both antecedent and consequent parameters update rules are derived based on the gradient descent method. The structure is applied in the identification of dynamic plants which is commonly used in the literature. Simulation result from the identification of a second-order non-linear plant confirms the better performance and effectiveness of the proposed structure.

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Nonlinear System Identification Using Type-2 Fuzzy Recurrent Wavelet Neural Network

In this paper, an alternative stability analysis for the Levenberg-Marquardt (LM) algorithm is proposed for the training of Type-2 fuzzy neural networks (T2FNNs). The benefit of the proposed stability analysis is that it does not require any eigenvalues computations, and hence it is simpler when compared to the existing stability analysis studies in literature.

Mojtaba Ahmadieh Khanesar

Papers

Observer-based indirect model reference fuzzy control system with application to control of chaotic systems

Recurrent Interval Type-2 Fuzzy Control of 2-DOF Helicopter With Finite Time Training Algorithm

Elliptic membership functions and the modeling uncertainty in type-2 fuzzy logic systems as applied to time series prediction

Nonlinear System Identification Using Type-2 Fuzzy Recurrent Wavelet Neural Network

Levenberg-Marquardt training method for Type-2 fuzzy neural networks and its stability analysis