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The use of flexible sigmoid functions makes artificial neural networks more versatile. This volume determines learning algorithms for sigmoid functions in several different learning modes using flexible structures of neural networks with new derivation algorithms. The book is aimed at electrical, electronic, and mechanical control and systems engineers concerned with intelligent control who wish to explore neural network approaches. Here, for readers who are unfamiliar with neural network computing, is a concise introduction to the main existing types of flexible neural networks. This work will be an aid to new research in which high abilities in artificial neural networks in intelligent control design and development can be achieved.
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