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Selection of input variables of fuzzy model using genetic algorithm with quick fuzzy inference
- Source :
- Lecture Notes in Computer Science ISBN: 9783540633990, SEAL
- Publication Year :
- 1997
- Publisher :
- Springer Berlin Heidelberg, 1997.
-
Abstract
- Hierarchical fuzzy modeling using fuzzy neural networks (FNN) is one of the effective approaches for modeling of nonlinear systems. Decision of antecedent structures of fuzzy models of nonlinear systems is made possible by a combination of FNN and genetic algorithm (GA). The disadvantage of this fuzzy modeling method is that the learning of FNN is time consuming. This paper presents an efficient fuzzy modeling method using simple fuzzy inference. The results of fuzzy modeling are heavily dependent on evaluation criteria. This paper also studies effects of evaluation criteria for the decision of the antecedent structure. Numerical experiments are done.
- Subjects :
- Adaptive neuro fuzzy inference system
Fuzzy classification
Neuro-fuzzy
Mathematics::General Mathematics
business.industry
Computer science
Fuzzy control system
computer.software_genre
Fuzzy logic
Defuzzification
Fuzzy set operations
Fuzzy number
ComputingMethodologies_GENERAL
Artificial intelligence
Data mining
business
computer
Subjects
Details
- ISBN :
- 978-3-540-63399-0
- ISBNs :
- 9783540633990
- Database :
- OpenAIRE
- Journal :
- Lecture Notes in Computer Science ISBN: 9783540633990, SEAL
- Accession number :
- edsair.doi...........9fc3fbc1d5b1cf4ad4e8cf6159cb4697
- Full Text :
- https://doi.org/10.1007/bfb0028520