Neural Network Systems Techniques and Applications. Volume 1. Algorithms and Architectures

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 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 This volume covers the integration of fuzzy logic and expert systems. A vital resource in the field, it includes techniques for applying fuzzy systems to neural networks for modeling and control, systematic design procedures for realizing fuzzy neural systems, techniques for the design of rule-based expert systems using the massively parallel processing capabilities of neural networks, the transformation of neural systems into rule-based expert systems, the characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets, and applications to system identification and control as well as nonparametric, nonlinear estimation.Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will appreciate this reference source to diverse application methodologies. There are many heuristic techniques described in the neural network literature to perform various tasks within the supervised learning paradigm, such as optimizing training, selecting an appropriately sized network, and predicting how much data will be required to achieve a particular generalization performance. v. 1. Algorithms and architectures v. 2. Optimization techniques v. 3. Implementation techniques v. 4. Industrial and manufacturing systems v. 5. Image processing and pattern recognition v. 6. Fuzzy logic and expert systems applications v. 7. Control and dynamic systems.