Abstract
A novel hybrid algorithm based on the genetic algorithm, named self-organizing fuzzy neural network based on genetic algorithm (SOFNNGA), is proposed to design a fuzzy neural network to implement Takagi- Sugeno (TS) type fuzzy models in this paper. A new adding method based on geometric growing criterion and the å-completeness of fuzzy rules is used to generate the initial structure firstly. Then a hybrid algorithm based on genetic algorithms, backpropagation, and recursive least squares estimation is used to adjust all parameters, which has two steps: first, adjusting the parameter matrix, and second, centers and widths of all membership functions are modified. A simulation for a benchmark problem is presented to illustrate the performance of the proposed algorithm.
| Original language | English |
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| Title of host publication | Unknown Host Publication |
| Pages | 1967-1972 |
| Number of pages | 6 |
| Publication status | Published (in print/issue) - Oct 2003 |
| Event | 2003 IEEE Int. Conf. Systems Man and Cybernetics - Washington, DC, USA Duration: 1 Oct 2003 → … |
Conference
| Conference | 2003 IEEE Int. Conf. Systems Man and Cybernetics |
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| Period | 1/10/03 → … |