Artificial neural networks are an alternative computational paradigm with roots in neurobiology which has attracted increasing interest in recent years. This book is a comprehensive introduction to the topic that stresses the systematic development of the underlying theory. Starting from simple threshold elements, more advanced topics are introduced, such as multilayer networks, efficient learning methods, recurrent networks, and self-organization. The various branches of neural network theory are interrelated closely and quite often unexpectedly, so the chapters treat the underlying connection between neural models and offer a unified view of the current state of research in the field.

The book has been written for anyone interested in understanding artificial neural networks or in learning more about them. The only mathematical tools needed are those learned during the first two years at university. The text contains more than 300 figures to stimulate the intuition of the reader and to illustrate the kinds of computation performed by neural networks. Material from the book has been used successfully for courses in Germany, Austria and the United States.

Contents

• The biological paradigm
• Threshold logic
• Weighted networks - The perceptron
• Perceptron learning
• Unsupervised learning and clustering algorithms
• One and two layered networks
• The back propagation algorithm
• Fast learning algorithms
• Statistics and neural networks
• The complexity of learning
• Fuzzy logic
• Associative networks
• The hopfield model
• Stochastic networks
• Kohonen networks
• Modular neural networks
• Genetic algorithms
• Hardware for neural networks