Name
Abstract | ||
|---|---|---|
This paper compares a genetic programming approach with a greedy partition algorithm (LOLIMOT) for structure identification of local linear neuro-fuzzy models. The crisp linear conclusion part of a Takagi-Sugeno-Kang (TSK) fuzzy rule describes the underlying model in the local region specified in the premise. The objective of structure identification is to identify an optimal partition of the input space into Gaussian, axis-orthogonal fuzzy sets. The linear parameters in the rule consequent are ... |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0020-0255(01)00139-6 | Inf. Sci. |
Keywords | Field | DocType |
tsk-fuzzy system,genetic programming,model selection,fuzzy set,neuro fuzzy,fuzzy system | Partition problem,Mathematical optimization,Defuzzification,Fuzzy classification,Fuzzy set operations,Genetic programming,Artificial intelligence,Fuzzy number,Membership function,Mathematics,Machine learning,Fuzzy rule | Journal |
Volume | Issue | ISSN |
136 | 1-4 | 0020-0255 |
Citations | PageRank | References |
33 | 1.61 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| frank hoffmann | 1 | 33 | 1.61 |
| Oliver Nelles | 2 | 99 | 17.27 |
| osterhofener str | 3 | 33 | 1.61 |