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This paper presents an orthogonal least squares (OLS) based modeling method, named dynamic OLS (D-OLS), for generating recurrent fuzzy models. A dynamic-neuron based fuzzy neural network is proposed, comprising generalized Takagi-Sugeno-Kang fuzzy rules, whose consequent parts consist of dynamic neurons with local output feedback. From an arbitrarily large set of candidate dynamic neurons, the D-OLS method selects automatically the most important ones. Thus, each fuzzy rule of the resulting model contains a different number and kind of dynamic neurons. In the simulation results, the effectiveness of the suggested method as well as the advantages of the resulting dynamic model are demonstrated
Published in:
Fuzzy Systems, 2001. The 10th IEEE International Conference on
(Volume:1
)
Date of Conference: 2001
- Page(s):
- 119 - 122
- Meeting Date :
- 02 Dec 2001-05 Dec 2001
- Print ISBN:
- 0-7803-7293-X
- INSPEC Accession Number:
- 7335168
- Conference Location :
- Melbourne, Vic.
- Digital Object Identifier :
- 10.1109/FUZZ.2001.1007261
- Product Type:
- Conference Publications
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