Generating fuzzy rules by learning from examples
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Citation edge evidence
- Fuzzy basis functions, universal approximation, and orthogonal least-squares learning→Generating fuzzy rules by learning from examplesLegacy edge — source detail unavailable
- Fuzzy Model Identification Based on Cluster Estimation→Generating fuzzy rules by learning from examplesLegacy edge — source detail unavailable
- Stable adaptive fuzzy control of nonlinear systems→Generating fuzzy rules by learning from examplesLegacy edge — source detail unavailable
- KEEL: a software tool to assess evolutionary algorithms for data mining problems→Generating fuzzy rules by learning from examplesLegacy edge — source detail unavailable
- Generating fuzzy rules by learning from examples→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
- Generating fuzzy rules by learning from examples→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
- Generating fuzzy rules by learning from examples→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
- Stable adaptive fuzzy control of nonlinear systems→Fuzzy basis functions, universal approximation, and orthogonal least-squares learningLegacy edge — source detail unavailable
- Fuzzy basis functions, universal approximation, and orthogonal least-squares learning→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
- Fuzzy basis functions, universal approximation, and orthogonal least-squares learning→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
- Fuzzy basis functions, universal approximation, and orthogonal least-squares learning→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
- Approximation by superpositions of a sigmoidal function→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
- Multilayer feedforward networks are universal approximators→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
- Multilayer feedforward networks are universal approximators→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Multilayer feedforward networks are universal approximators | 1989 | 21,633 |
| Approximation by superpositions of a sigmoidal function | 1989 | 13,884 |
| Multilayer feedforward networks are universal approximators | 1989 | 9,357 |
What cites it, inside the corpus
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Topics
| Fuzzy Logic and Control Systems | Computer Science |
| Neural Networks and Applications | Computer Science |
| Rough Sets and Fuzzy Logic | Computer Science |
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