Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 16, Issue 5, Pages 1362-1378Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2008.924334
Keywords
Entropy; functional link neural networks (FLNNs); neurofuzzy networks (NFNs); nonlinear system control; online learning
Funding
- Ministry of Economic Affairs, Taiwan, R.O.C. [96-EC-17-A-02-S1-032]
- National Science Council, Taiwan, R.O.C. [NSC 95-2221-E009-180]
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This study presents a functional-link-based neurofuzzy network (FLNFN) structure for nonlinear system control. The proposed FLNFN model uses a functional link neural network (FLNN) to the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the FLNN. Thus, the consequent part of the proposed FLNFN model is a nonlinear combination of input variables. An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the gradient descent method, can adjust the shape of the membership function and the corresponding weights of the FLNN. Furthermore, results for the universal approximator and a convergence analysis of the FLNFN model are proven. Finally, the FLNFN model is applied in various simulations. Results of this study demonstrate the effectiveness of the proposed FLNFN model.
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