| Soft Computing in Communications 2004 Edition Contributor(s): Wang, Lipo (Editor) |
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ISBN: 3540405755 ISBN-13: 9783540405757 Publisher: Springer OUR PRICE: $161.49 Product Type: Hardcover - Other Formats Published: September 2003 Annotation: This book is dedicated to recent novel applications of soft computing in communications. It presents the methodologies of neural networks, evolutionary computation, fuzzy logic and neurofuzzy systems, and kernel methods. Applications to the wide field of communications are demonstrated, such as to multiservice access multiplexers, hierarchical cellular systems, multicast routing, optimal channel assignment to cellular mobile communications, transporting voice over a wireless LAN, or the design of communication antennas. |
| Additional Information |
| BISAC Categories: - Computers | Intelligence (ai) & Semantics - Mathematics | Applied - Technology & Engineering | Electrical |
| Dewey: 006.3 |
| LCCN: 2003057381 |
| Series: Studies in Fuzziness and Soft Computing |
| Physical Information: 0.94" H x 6.14" W x 9.21" (1.68 lbs) 408 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Soft computing, as opposed to conventional "hard" computing, tolerates imprecision and uncertainty, in a way very much similar to the human mind. Soft computing techniques include neural networks, evolutionary computation, fuzzy logic, and chaos. The recent years have witnessed tremendous success of these powerful methods in virtually all areas of science and technology, as evidenced by the large numbers of research results published in a variety of journals, conferences, as weil as many excellent books in this book series on Studies in Fuzziness and Soft Computing. This volume is dedicated to recent novel applications of soft computing in communications. The book is organized in four Parts, i.e., (1) neural networks, (2) evolutionary computation, (3) fuzzy logic and neurofuzzy systems, and (4) kernel methods. Artificial neural networks consist of simple processing elements called neurons, which are connected by weights that may be adjusted during learning. Part 1 of the book has seven chapters, demonstrating some of the capabilities of two major types of neural networks, i.e., multiplayer perceptron (MLP) neural networks and Hopfield-type neural networks. |