Analysis and Applications of Artificial Neural Networks

Front Cover
Prentice Hall, Jan 1, 1995 - Computers - 259 pages
Thorough, compact, and self-contained, this explanation and analysis of a broad range of neural nets is conveniently structured so that readers can first gain a quick global understanding of neural nets -- without the mathematics -- and can then delve into mathematical specifics as necessary. The behavior of neural nets is first explained from an intuitive perspective; the formal analysis is then presented; and the practical implications of the formal analysis are stated separately. Analyzes the behavior of the six main types of neural networks -- The Binary Perceptron, The Continuous Perceptron (Multi-Layer Perceptron), The Bidirectional Memories, The Hopfield Network (Associative Neural Nets), The Self-Organizing Neural Network of Kohonen, and the new Time Sequentional Neural Network. For technically-oriented individuals working with information retrieval, pattern recognition, speech recognition, signal processing, data classification.

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The continuous multilayer Perceptron
of a sine wave

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