Neural Network Parallel Computing

ISBN-13: 9781461366201 / Angielski / Miękka / 2012 / 230 str.

Opis

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.

Recenzje

` This is really the first good book on neural computing for optimization problems.
Neural Network Parallel Computing is an excellent reference for researchers working on optimization problems, and for engineers searching for practical problem-solving methods.
Overall, the book is a fascinating presentation of a very difficult problem of contemporary science. '
IEEE Parallel & Distributed Technology, Fall 1994
'However, the book does provide many useful algorithms and applications of neural networks. It is a worthwhile addition to a library of neutral network research.' Journal of systems and Control Engineering 206 1992

Spis treści

Foreword. 1. Neural Network Models and N-queen Problems. 2. Crossbar Switch Scheduling Problems. 3. Four-Colouring and K-Colorability Problems. 4. Graph Planarization Problems. 5. Channel Routing Problems. 6. RNA Secondary Structure Prediction. 7. Knight's Tour Problems. 8. Spare Allocation Problems. 9. Sorting and Searching. 10. Tiling Problems. 11. Silicon Neural Networks. 12. Mathematical Background. 13. Forthcoming Applications. 14. Conjunctoids and Artificial Learning. Subject Index.