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Full Description
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.
Contents
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.
