Neural Network Computing
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Neural Network Computing
Author: Ramachandran Bharath
language: en
Publisher: McGraw-Hill Companies
Release Date: 1994
An introduction to neural networking for systems designers, software developers, programmers, and advanced hobbyists. The authors explain how "brain-style" computing will revolutionize information processing in the 21st century. The disk includes programs for simulating artificial neural networks.
Neural Network Parallel Computing
Author: Yoshiyasu Takefuji
language: en
Publisher: Springer Science & Business Media
Release Date: 1992-01-31
Neural Network Parallel Computing is the first book available to the professional market on neural network computing for optimization problems. This introductory book is not only for the novice reader, but for experts in a variety of areas including parallel computing, neural network computing, computer science, communications, graph theory, computer aided design for VLSI circuits, molecular biology, management science, and operations research. The goal of the book is to facilitate an understanding as to the uses of neural network models in real-world applications. Neural Network Parallel Computing presents a major breakthrough in science and a variety of engineering fields. The computational power of neural network computing is demonstrated by solving numerous problems such as N-queen, crossbar switch scheduling, four-coloring and k-colorability, graph planarization and channel routing, RNA secondary structure prediction, knight's tour, spare allocation, sorting and searching, and tiling. Neural Network Parallel Computing is an excellent reference for researchers in all areas covered by the book. Furthermore, the text may be used in a senior or graduate level course on the topic.
Neural Networks and Analog Computation
Author: Hava Siegelmann
language: en
Publisher: Springer Science & Business Media
Release Date: 1998-12-01
The theoretical foundations of Neural Networks and Analog Computation conceptualize neural networks as a particular type of computer consisting of multiple assemblies of basic processors interconnected in an intricate structure. Examining these networks under various resource constraints reveals a continuum of computational devices, several of which coincide with well-known classical models. On a mathematical level, the treatment of neural computations enriches the theory of computation but also explicated the computational complexity associated with biological networks, adaptive engineering tools, and related models from the fields of control theory and nonlinear dynamics. The material in this book will be of interest to researchers in a variety of engineering and applied sciences disciplines. In addition, the work may provide the base of a graduate-level seminar in neural networks for computer science students.