Neural Computing - An Introduction

INTRODUCTION Humans and computers The structure of the brain Learning in machines The differences Summary PATTERN RECOGNITION Introduction Pattern recognition in perspective Pattern recognition-a definition Feature vectors and feature space Discriminant functions Classification techniques Linear classifiers Statistical techniques Pattern recognition-a summary THE BASIC NEURON Introduction Modeling the single neuron Learning in simple neurons The perceptron: a vectorial perspective The perceptron learning rule: proof Limitations of perceptrons The end of the line? Summary THE MULTILAYER PERCEPTRON Introduction Altering the perceptron model The new model The new learning rule The multilayer perceptron algorithm The XOR problem revisited Visualizing network behavior Multilayer perceptrons as classifiers Generalization Fault tolerance Learning difficulties Radial basis functions Applications Summary KOHONEN SELF-ORGANIZING NETWORKS Introduction The Kohonen algorithm Weight training Neighborhoods Reducing the neighborhood Learning vector quantization (LVQ) The phonetic typewriter Summary HOPFIELD NETWORKS Introduction The Hopfield model The energy landscape The Boltzmann machine Constraint satisfaction Summary ADAPTIVE RESONANCE THEORY Introduction Adaptive resonance theory (ART) Architecture and operation ART algorithm Training the ART network Classification Conclusion Summary of ART ASSOCIATIVE MEMORY Standard computer memory Implementing associative memory Implementation in RAMs RAMs and N-tupling Willshaw's associative net The ADAM system Kanerva's sparse distributed memory Bidirectional associative memories Conclusion Summary INTO THE LOOKING GLASS Overview Hardware and software implementations Optical computing Optical computing and neural networks INDEX