Genetic Learning of Serial Hierarchical Fuzzy Systems for Large-Scale Problems
作者： Jorge Casillas , Alicia D. Benand
DOI:
关键词: Genetic algorithm 、 Mathematics 、 Scale (ratio) 、 Interpretability 、 Fuzzy classification 、 Curse of dimensionality 、 Machine learning 、 Face (geometry) 、 Neuro-fuzzy 、 Artificial intelligence 、 Fuzzy control system
摘要: When we face a problem with high number of vari- ables by standard fuzzy system, the rules increases expo- nentially and then obtained system is scarcely interpretable. This can be handled arranging inputs in hierarchical ways. The paper presents multi-objective Genetic Algorithm that learns Serial Hierarchical Fuzzy Systems aim coping curse dimensionality. By means an experimental study, have observed our algorithm obtains good results interpretability precision problems which variables rel- atively high.
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