Neural Networks

• 1. The Biological Paradigm.- 1.1 Neural computation.- 1.2 Networks of neurons.- 1.3 Artificial neural networks.- 1.4 Historical and bibliographical remarks.- 2. Threshold Logic.- 2.1 Networks of functions.- 2.2 Synthesis of Boolean functions.- 2.3 Equivalent networks.- 2.4 Recurrent networks.- 2.5 Harmonic analysis of logical functions.- 2.6 Historical and bibliographical remarks.- 3.Weighted Networks — The Perceptron.- 3.1 Perceptrons and parallel processing.- 3.2 Implementation of logical functions.- 3.3 Linearly separable functions.- 3.4 Applications and biological analogy.- 3.5 Historical and bibliographical remarks.- 4. Perceptron Learning.- 4.1 Learning algorithms for neural networks.- 4.2 Algorithmic learning.- 4.3 Linear programming.- 4.4 Historical and bibliographical remarks.- 5. Unsupervised Learning and Clustering Algorithms.- 5.1 Competitive learning.- 5.2 Convergence analysis.- 5.3 Principal component analysis.- 5.4 Some applications.- 5.5 Historical and bibliographical remarks.- 6. One and Two Layered Networks.- 6.1 Structure and geometric visualization.- 6.2 Counting regions in input and weight space.- 6.3 Regions for two layered networks.- 6.4 Historical and bibliographical remarks.- 7. The Backpropagation Algorithm.- 7.1 Learning as gradient descent.- 7.2 General feed-forward networks.- 7.3 The case of layered networks.- 7.4 Recurrent networks.- 7.5 Historical and bibliographical remarks.- 8. Fast Learning Algorithms.- 8.1 Introduction — classical backpropagation.- 8.2 Some simple improvements to backpropagation.- 8.3 Adaptive step algorithms.- 8.4 Second-order algorithms.- 8.5 Relaxation methods.- 8.6 Historical and bibliographical remarks.- 9. Statistics and Neural Networks.- 9.1 Linear and nonlinear regression.- 9.2 Multiple regression.- 9.3Classification networks.- 9.4 Historical and bibliographical remarks.- 10. The Complexity of Learning.- 10.1 Network functions.- 10.2 Function approximation.- 10.3 Complexity of learning problems.- 10.4 Historical and bibliographical remarks.- 11. Fuzzy Logic.- 11.1 Fuzzy sets and fuzzy logic.- 11.2 Fuzzy inferences.- 11.3 Control with fuzzy logic.- 11.4 Historical and bibliographical remarks.- 12. Associative Networks.- 12.1 Associative pattern recognition.- 12.2 Associative learning.- 12.3 The capacity problem.- 12.4 The pseudoinverse.- 12.5 Historical and bibliographical remarks.- 13. The Hopfield Model.- 13.1 Synchronous and asynchronous networks.- 13.2 Definition of Hopfield networks.- 13.3 Converge to stable states.- 13.4 Equivalence of Hopfield and perceptron learning.- 13.5 Parallel combinatorics.- 13.6 Implementation of Hopfield networks.- 13.7 Historical and bibliographical remarks.- 14. Stochastic Networks.- 14.1 Variations of the Hopfield model.- 14.2 Stochastic systems.- 14.3 Learning algorithms and applications.- 14.4 Historical and bibliographical remarks.- 15. Kohonen Networks.- 15.1 Self-organization.- 15.2 Kohonen’s model.- 15.3 Analysis of convergence.- 15.4 Applications.- 15.5 Historical and bibliographical remarks.- 16. Modular Neural Networks.- 16.1 Constructive algorithms for modular networks.- 16.2 Hybrid networks.- 16.3 Historical and bibliographical remarks.- 17. Genetic Algorithms.- 17.1 Coding and operators.- 17.2 Properties of genetic algorithms.- 17.3 Neural networks and genetic algorithms.- 17.4 Historical and bibliographical remarks.- 18. Hardware for Neural Networks.- 18.1 Taxonomy of neural hardware.- 18.2 Analog neural networks.- 18.3 Digital networks.- 18.4 Innovative computer architectures.- 18.5 Historical and bibliographicalremarks.

"If you want a systematic and thorough overview of neural networks, need a good reference book on this subject, or are giving or taking a course on neural networks, this book is for you." Computing Reviews