Computational Intelligence in Control

Computational Intelligence in Control

Masoud Mohammadian (University of Canberra, Australia), Rahul A. Sarker (University of New South Wales, Australia) and Xin Yao (The University of Birmingham, UK)
Release Date: July, 2002|Copyright: © 2003 |Pages: 348
DOI: 10.4018/978-1-59140-037-0
ISBN13: 9781591400370|ISBN10: 1591400376|EISBN13: 9781591400790
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Description & Coverage
Description:

The problem of controlling uncertain dynamic systems, which are subject to external disturbances, uncertainty and sheer complexity is of considerable interest in computer science, Operations Research and Business domains. The application of intelligent systems has been found useful in problems when the process is either difficult to model or difficult to solve by conventional methods. Intelligent systems have attracted increasing attention in recent years for solving many complex problems. Computational Intelligence in Control will be a repository for the theory and applications of intelligent systems techniques in modelling control and automation.

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Reviews & Statements

The book Computational Intelligence in Control presents many theoretical findings in the area of computational intelligence and its applications from leading researchers. The book is informative and serves as a very good resource for the training, designing, and analysis of computational intelligence techniques, including neural network, fuzzy logic, and evolutionary computation. I strongly recommend this book to all readers in the field.

– Dr. Kay Chen Tan, Department of Electrical and Computer Engineering, National University of Singapore
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Editor/Author Biographies

Masoud Mohammadian has completed his Bachelor, Master and PhD in Computer Science. His research interests lie in adaptive self-learning systems, fuzzy logic, genetic algorithms, neural networks and their applications in robotics, control, industrial automation, financial and business problems which involve real time data processing, planning and decision making. He is a member of over 30 international conferences and he has chaired several international conferences in computational intelligence and intelligent agents. He is currently a senior lecturer at the school of computing at the University of Canberra in Australia. He is a member of many professional (computing and engineering) organizations . He is also currently the vice chair of the Institute of Electrical and Electronic Engineering (IEEE) ACT section.

Ruhul Sarker received his PhD in 1991 from DalTech, Dalhousie University, Halifax, Canada, and is currently a senior lecturer in Operations Research at the School of Computer Science, University of New South Wales, ADFA Campus, Canberra, Australia. Before joining at UNSW in February 1998, Dr. Sarker worked with Monash University, Victoria, and the Bangladesh University of Engineering and Technology, Dhaka. His main research interests are Evolutionary Optimization, Data Mining and Applied Operations Research. He was involved with three edited books either as editor or co-editor, and has published more than 80 refereed papers in international journals and conference proceedings. He is also the editor of ASOR Bulletin, the national publication of the Australian Society for Operations Research.

Xin Yao received the BSc degree in computer science from the University of Science and Technology of China (USTC), Hefei, the MSc degree in computer science from the North China Institute of Computing Technologies (NCI), Beijing, and the PhD degree in computer science from the USTC, Hefei, in 1982, 1985, and 1990, respectively. He is currently a professor of computer science at the University of Birmingham, Birmingham, England. Xin Yao is an associate editor or a member of the editorial board of six international journals, including IEEE Transactions on Evolutionary Computation, and an editor/co-editor of nine journal special issues. His major research interests include combinations between neural and evolutionary computation techniques, evolutionary learning, co-evolution, evolutionary design and evolvable hardware, neural network ensembles, global optimization, simulated annealing, computational time complexity and data mining.

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