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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jyh-Yeong Chang Yang-Yin Lin Ming-Feng Han Chin-Teng Lin |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Electrical and Control Engineering, National Chiao-Tung University, Hsin-Chu, Taiwan, R.O.C. (Jyh-Yeong Chang; Yang-Yin Lin; Ming-Feng Han; Chin-Teng Lin) |
| Abstract | In this paper, the Functional-Link based Interval Type-2 Compensatory Fuzzy Neural Network (FLIT2CFNN) is a six-layer structure, which combines compensatory fuzzy reasoning method, and the consequent part is combined the proposed functional-link neural network with interval weights. The compensatory fuzzy reasoning method uses adaptive fuzzy operations of neuro-fuzzy systems that can make the fuzzy logic system more adaptive and effective. Initially, there is no rule in the FLIT2CFNN. A FLIT2CFNN is constructed using concurrent structure and parameter learning. The advantages of this learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. All of the antecedent part parameters and compensatory degree values are learned by gradient descent algorithm. Several simulation results show that the FLIT2CFNN achieves better performance than other feedforword type-1 and type-2 FNNs. |
| Starting Page | 939 |
| Ending Page | 943 |
| File Size | 584994 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424473151 |
| ISSN | 10987584 |
| e-ISBN | 9781424473175 |
| e-ISBN | 9781424473168 |
| DOI | 10.1109/FUZZY.2011.6007477 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-27 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fuzzy neural networks Adaptive systems Firing Noise Fuzzy logic Nonlinear systems on-line fuzzy clustering type-2 fuzzy systems compensatory operation structure learning |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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