Neural Networks: An Introduction

✍ Scribed by Professor Dr. Berndt Müller, Dr. Joachim Reinhardt (auth.)

• Publisher: Springer Berlin Heidelberg
• Year: 1990
• Tongue: English
• Leaves: 278
• Series: Physics of Neural Networks
• Category: Library

✦ Table of Contents

Front Matter....Pages I-XIII
Front Matter....Pages 1-1
The Structure of the Central Nervous System....Pages 2-11
Neural Networks Introduced....Pages 12-22
Associative Memory....Pages 23-36
Stochastic Neurons....Pages 37-44
Cybernetic Networks....Pages 45-50
Multilayered Perceptrons....Pages 51-61
Applications....Pages 62-72
Network Architecture and Generalization....Pages 73-86
Associative Memory: Advanced Learning Strategies....Pages 87-103
Combinatorial Optimization....Pages 104-112
VLSI and Neural Networks....Pages 113-118
Symmetrical Networks with Hidden Neurons....Pages 119-125
Coupled Neural Networks....Pages 126-131
Unsupervised Learning....Pages 132-144
Front Matter....Pages 145-145
Statistical Physics and Spin Glasses....Pages 146-155
The Hopfield Network for p / N → o....Pages 156-164
The Hopfield Network for Finite p / N ....Pages 165-186
The Space of Interactions in Neural Networks....Pages 187-202
Front Matter....Pages 203-203
Numerical Demonstrations....Pages 204-207
ASSO : Associative Memory....Pages 208-217
Front Matter....Pages 203-203
ASSCOUNT : Associative Memory for Time Sequences....Pages 218-221
PERBOOL : Learning Boolean Functions with Back-Propagation....Pages 222-228
PERFUNC : Learning Continuous Functions with Back-Propagation....Pages 229-232
Solution of the Traveling-Salesman Problem....Pages 233-244
KOHOMAP : The Kohonen Self-organizing Map....Pages 245-249
Back Matter....Pages 250-266

✦ Subjects

Thermodynamics; Statistical Physics, Dynamical Systems and Complexity; Artificial Intelligence (incl. Robotics); Neurosciences