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

A Neural Network Separation Approach for the Inclusion of Static Friction in Nonlinear Static Models of Industrial Robots

The use of data networks in control loops has received much attention recently due to its flexibility and economical advantages. In addition, mutual network usage has raised new challenges such as delay and data loss. This paper aims to reduce undesired effects of network by reducing the required traffic of the network. An estimation framework for network control system is introduced, in which estimations of local Kalman filter is sent to remote estimator based on the logic decided by a novel fuzzy communication logic. In order to do so, there exist two estimators, a remote estimator which estimates the states of the plant and its local copy that gives the same output. The output of the local estimator is compared with the real states of the system, if the states of the system are estimated with small error, there is no need to send data, hence, the probability of sending data is decreased using a fuzzy decision system. In order to optimize this fuzzy system, a particle swarm optimization (PSO) algorithm ...

Improving the quality of service in network-based control systems:

This study presents the normative knowledge source for the belief space of cultural algorithm(CA) based on an adaptive Radial Basis Function Neural Network (RBFNN). The use of the RBFNN makes it possible to use the previous upper and lower bounds of the normative knowledge to update them and to extract a logical relationship between the previous parameters of the normative knowledge and their new values. The proposed algorithm(N3KCA) is similar to what the human brain does, i.e. to predict the new values of the bounds of normative knowledge based on the previous ones and some knowledge, which it has gained from the previous successive updates. Finally, the proposed cultural algorithm is evaluated on 10 unimodal and multimodal benchmark functions. The algorithm is compared with several other optimization algorithms including previous version of cultural algorithm. In order to have a fair comparison, the number of cost function evaluation is kept the same for all optimization algorithms. The obtaine...

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Neural Networks for Normative Knowledge Source of Cultural Algorithm

In this chapter, an attempt is made to show the effect of input noise in the rule base in a general way. There exist number of papers in literature claiming that the performance of T2FLSs is better than its type-1 counterparts under noisy conditions. We try to justify this claim by simulation studies only for some specific systems. However, in this chapter, such an analysis is done independent of the system to be controlled. For such an analysis, a novel type-2 fuzzy MF (elliptic MF) is proposed. This type-2 MF has certain values on both ends of the support and the kernel, and some uncertain values for other values of the support. The findings of the general analysis in this chapter and the aforementioned studies published in literature are coherent.

Mojtaba Ahmadieh Khanesar

Papers

A Neural Network Separation Approach for the Inclusion of Static Friction in Nonlinear Static Models of Industrial Robots

Improving the quality of service in network-based control systems:

Neural Networks for Normative Knowledge Source of Cultural Algorithm

Noise Reduction Property of Type-2 Fuzzy Neural Networks

MOSCAP compensation of three-stage operational amplifiers: Sensitivity and robustness, modeling and analysis