Modeling and control of non-linear systems using soft computing techniques
| Autor: | Mouloud Denai, F. Palis, Abdelhafid Zeghbib |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Soft computing
Adaptive neuro fuzzy inference system Neuro-fuzzy Artificial neural network Computer science business.industry Evolutionary algorithm Complex system Machine learning computer.software_genre Fuzzy logic Nonlinear system ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence business Intelligent control computer Software |
| Zdroj: | Applied Soft Computing. 7:728-738 |
| ISSN: | 1568-4946 |
| Popis: | This work is an attempt to illustrate the utility and effectiveness of soft computing approaches in handling the modeling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, evolutionary algorithms, ...) in a complementary hybrid framework for solving real world problems. There are several approaches to integrate neural networks and fuzzy logic to form a neuro-fuzzy system. The present work will concentrate on the pioneering neuro-fuzzy system, Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is first used to model non-linear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behavior of the underlying system and for the design and evaluation of various intelligent control strategies. |
| Databáze: | OpenAIRE |
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