Neural Networks – A Systematic Introduction

Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets.

Book Description

Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.

Table of Contents

• The biological paradigm
• Threshold logic
• Weighted Networks – The Perceptron
• Perceptron learning
• Unsupervised learning and clustering algorithms
• One and two layered networks
• The backpropagation 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 Network
• Genetic Algorithms
• Hardware for neural networks

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