Name
Title | ||
|---|---|---|
Selection of Input Variables of Fuzzy Model Using Genetic Algorithm with Quick Fuzzy Inference |
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. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1007/BFb0028520 | SEAL |
Keywords | Field | DocType |
fuzzy neural network,nonlinear modeling,genetic algorithm,input variables,quick fuzzy inference,fuzzy model,fuzzy modeling,nonlinear system | Data mining,Neuro-fuzzy,Fuzzy classification,Defuzzification,Fuzzy set operations,Computer science,Fuzzy logic,Artificial intelligence,Fuzzy control system,Adaptive neuro fuzzy inference system,Fuzzy number,Machine learning | Conference |
ISBN | Citations | PageRank |
3-540-63399-5 | 2 | 0.47 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seiichi Matsushita | 1 | 24 | 4.34 |
| Takeshi Furuhashi | 2 | 2 | 0.47 |
| H. Tsutsui | 3 | 2 | 0.47 |
| Yoshiki Uchikawa | 4 | 455 | 72.31 |