Adaptive neuro-fuzzy control system
With the growing interest of using fuzzy logic in our world, adaptive fuzzy logic is keenly researched in the recent decades. One promising way of making fuzzy logic adaptable is to blend it with neural network, which itself is inherently suited to self-learning application. Neural fuzzy systems are...
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| Format: | Theses and Dissertations |
| Published: |
2008
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| Online Access: | http://hdl.handle.net/10356/4105 |
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| Institution: | Nanyang Technological University |
| Summary: | With the growing interest of using fuzzy logic in our world, adaptive fuzzy logic is keenly researched in the recent decades. One promising way of making fuzzy logic adaptable is to blend it with neural network, which itself is inherently suited to self-learning application. Neural fuzzy systems are frequently used in control applications (Lin and Lee, 1996). These are fuzzy systems implemented with neural networks. The two prominent systems are Adaptive Neuro-Fuzzy Inference System (ANFIS) by Jang (1993) and Fuzzy Adaptive Learning Control Network (FALCON) by Lin (1994). These systems represent two main approaches to implement adaptive neural fuzzy systems. However, there is no comparison being carried out between them. In this dissertation, we shall compare their relative features and performance. |
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