Neural Networks in a Softcomputing Framework [Paperback]

Conventional model-based data processing methods are computationally expensive and require experts knowledge for the modelling of a system. Neural networks are a model-free, adaptive, parallel-processing solution. This textbook provides a powerful and universal paradigm for information processing; it reviews the most popular neural-network methods and their associated techniques.

Each chapter has a systematic survey of each neural-network model. Computational intelligence topics like fuzzy logic and genetic algorithms (tools for neural-network learning) are introduced. Array signal processing problems are used to show the applications of each model.

This is an ideal textbook for graduate students and researchers; as well as introducing the basics, the exhaustive list of references included will aid their future research. It is also a valuable reference for scientists and practitioners working in pattern recognition, signal processing, speech and image processing, data analysis and A.I.

Fundamentals of Machine Learning and Softcomputing.- Multilayer Perceptrons.- Hopfield Networks and Boltzmann Machines.- Competitive Learning and Clustering.- Radial Basis Function Networks.- Principal Component Analysis Networks.- Fuzzy Logic and Neurofuzzy Systems.- Evolutionary Algorithms and Evolving Neural Networks.- Discussion and Outlook.

Dr. K. -L. Du  received the B. Eng. degree from Tianjin University of Science and Technology in 1992, and the M.A.Sc. and Ph.D. degrees from Huazhong University of Science and Technology (HUST), China, in 1995 and 1998 respectively. From 1998, he was employed by Huawei Technologies, and then by China Academy of Telecommunications Technology (CATT), as a Research Engineer for wireless communications and signal processing. From 2000 to 2001, he served as the assistant computer officer (project manager) with Microwave and Communication Laboratory at The Chinese University of l“X