| Description |
x, 305 pages : illustrations |
| Contents |
1. Introduction -- 2. Historical and Biological Aspects -- 3. A Generic Model of Neural Networks -- 4. Neural Networks -- 5. Fuzzy Systems -- 6. Modelling Neuro-Fuzzy Systems -- 7. Cooperative Neuro-Fuzzy Systems -- 8. Hybrid Neuro-Fuzzy Systems -- 9. The Generic Fuzzy Perceptron -- 10. NEFCON - Neuro-Fuzzy Control -- 11. NEFCLASS - Neuro-Fuzzy Classification --12. NEFPROX - Neuro-Fuzzy Function Approximation --13. Neural Networks and Fuzzy Prolog -- 14. Using Neuro-Fuzzy Systems |
| Summary |
Foundations of Neuro-Fuzzy Systems reflects the current trend in intelligent systems research towards the integration of neural networks and fuzzy technology. The authors demonstrate how a combination of both techniques enhances the performance of control, decision-making and data analysis systems. Smarter and more applicable structures result from marrying the learning capability of the neural network with the transparency and interpretability of the rule-based fuzzy system. Foundations of Neuro-Fuzzy Systems highlights the advantages of integration making it a valuable resource for graduate students and researchers in control engineering, computer science and applied mathematics. The authors' informed analysis of practical neuro-fuzzy applications will be an asset to industrial practitioners using fuzzy technology and neural networks for control systems, data analysis and optimization tasks |
| Notes |
Translation of: Neuronale Netze und Fuzzy Systeme. 2nd ed |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Neural networks (Computer science)
|
|
Fuzzy systems.
|
| Author |
Kruse, Rudolf.
|
|
Klawonn, F.
|
| LC no. |
97030674 |
| ISBN |
0471971510 |
|
