This volume is the first diverse and comprehensive treatment of algorithms and architectures for the realization of neural network systems. It presents techniques and diverse methods in numerous areas of this broad subject. The book covers major neural network systems structures for achieving effective systems, and illustrates them with examples.
This volume includes Radial Basis Function networks, the Expand-and-Truncate Learning algorithm for the synthesis of Three-Layer Threshold Networks, weight initialization, fast and efficient variants of Hamming and Hopfield neural networks, discrete time synchronous multilevel neural systems with reduced VLSI demands, probabilistic design techniques, time-based techniques, techniques for reducing physical realization requirements, and applications to finite constraint problems.
A unique and comprehensive reference for a broad array of algorithms and architectures, this book will be of use to practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as in computer science and engineering.
Key Features
Radial Basis Function networks
The Expand-and-Truncate Learning algorithm for the synthesis of Three-Layer Threshold Networks
Weight initialization
Fast and efficient variants of Hamming and Hopfield neural networks
Discrete time synchronous multilevel neural systems with reduced VLSI demands
Probabilistic design techniques
Time-based techniques
Techniques for reducing physical realization requirements
Applications to finite constraint problems
Practical realization methods for Hebbian type associative memory systems
Parallel self-organizing hierarchical neural network systems
Dynamics of networks of biological neurons for utilization in computational neuroscience
Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as in computer science and engineering, will find this volume a unique and comprehensive reference to a broad array of algorithms and architectures
Inspired by the structure of the human brain, artificial neural networks have found many applications due to their ability to solve cumbersome or intractable problems by learning directly from data. Neural networks can adapt to new environments by learning, and deal with information that is noisy, inconsistent, vague, or probabilistic. This volume of Neural Network Systems Techniques and Applications is devoted to Algorithms and Architectures for the realization of artificial neural networks.
Hardcover: 460 pages
Publisher: Academic Press; 1st edition (October 27, 1997)
Language: English
ISBN-10: 012443861X
ISBN-13: 978-0124438613