| Neural Networks and Fuzzy Systems: Theory and Applications 1997 Edition Contributor(s): Abe, Shigeo (Author) |
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ISBN: 0792398149 ISBN-13: 9780792398141 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: November 1996 Annotation: Neural Networks and Fuzzy Systems: Theory and Applications discusses theories that have proven useful in applying neural networks and fuzzy systems to real world problems. The book includes performance comparison of neural networks and fuzzy systems using data gathered from real systems. Topics covered include the Hopfield network for combinatorial optimization problems, multilayered neural networks for pattern classification and function approximation, fuzzy systems that have the same functions as multilayered networks, and composite systems that have been successfully applied to real world problems. The author also includes representative neural network models such as the Kohonen network and radial basis function network. New fuzzy systems with learning capabilities are also covered. The advantages and disadvantages of neural networks and fuzzy systems are examined. The performance of these two systems in license plate recognition, a water purification plant, blood cell classification, and other real world problems is compared. |
| Additional Information |
| BISAC Categories: - Computers | Neural Networks - Computers | Intelligence (ai) & Semantics - Computers | Networking - General |
| Dewey: 006.3 |
| LCCN: 96043309 |
| Physical Information: 0.69" H x 6.14" W x 9.21" (1.27 lbs) 258 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Neural Networks and Fuzzy Systems: Theory and Applications discusses theories that have proven useful in applying neural networks and fuzzy systems to real world problems. The book includes performance comparison of neural networks and fuzzy systems using data gathered from real systems. Topics covered include the Hopfield network for combinatorial optimization problems, multilayered neural networks for pattern classification and function approximation, fuzzy systems that have the same functions as multilayered networks, and composite systems that have been successfully applied to real world problems. The author also includes representative neural network models such as the Kohonen network and radial basis function network. New fuzzy systems with learning capabilities are also covered. The advantages and disadvantages of neural networks and fuzzy systems are examined. The performance of these two systems in license plate recognition, a water purification plant, blood cell classification, and other real world problems is compared. |