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: | Thesis |
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2008
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| Online Access: | http://hdl.handle.net/10356/4105 |
| _version_ | 1811684640091537408 |
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| author | Chern, Wen Kwang. |
| author2 | Chin, Teck Chai |
| author_facet | Chin, Teck Chai Chern, Wen Kwang. |
| author_sort | Chern, Wen Kwang. |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:31:50Z |
| format | Thesis |
| id | ntu-10356/4105 |
| institution | Nanyang Technological University |
| last_indexed | 2024-10-01T04:31:50Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | ntu-10356/41052023-07-04T15:57:35Z Adaptive neuro-fuzzy control system Chern, Wen Kwang. Chin, Teck Chai School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering 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. Master of Science 2008-09-17T09:44:35Z 2008-09-17T09:44:35Z 2000 2000 Thesis http://hdl.handle.net/10356/4105 Nanyang Technological University application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Chern, Wen Kwang. Adaptive neuro-fuzzy control system |
| title | Adaptive neuro-fuzzy control system |
| title_full | Adaptive neuro-fuzzy control system |
| title_fullStr | Adaptive neuro-fuzzy control system |
| title_full_unstemmed | Adaptive neuro-fuzzy control system |
| title_short | Adaptive neuro-fuzzy control system |
| title_sort | adaptive neuro fuzzy control system |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering |
| url | http://hdl.handle.net/10356/4105 |
| work_keys_str_mv | AT chernwenkwang adaptiveneurofuzzycontrolsystem |