Name
Abstract | ||
|---|---|---|
Hierarchical fuzzy modeling is a promising technique to describe input-output relationships of nonlinear systems with multiple inputs. This paper presents a new method of dividing input spaces for hierarchical fuzzy modeling using Fuzzy Neural Network (FNN) and Genetic Algorithm (GA). Uneven division of input space for each submodel in the hierarchical fuzzy model can be achieved with the proposed method. The obtained hierarchical fuzzy models are probable to be more concise and more precise than those identified with the conventional methods. Studies on effects of the weights on performance indices for the fuzzy model are also shown in this paper. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/3-540-48873-1_28 | SEAL |
Keywords | Field | DocType |
multiple input,fuzzy model,conventional method,input space,hierarchical fuzzy modeling,genetic algorithm,fuzzy neural network,new method,hierarchical fuzzy model,input output,performance indicator,nonlinear system | Neuro-fuzzy,Defuzzification,Fuzzy classification,Fuzzy set operations,Computer science,Fuzzy logic,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy associative matrix,Fuzzy rule | Conference |
Volume | ISSN | ISBN |
1585 | 0302-9743 | 3-540-65907-2 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kanta Tachibana | 1 | 12 | 4.81 |
| Takeshi Furuhashi | 2 | 0 | 0.34 |