Title :
Automatic simultaneous architecture and parameter search in fuzzy neural network learning using novel variable length crossover differential evolution
Author :
Singh, Lotika ; Kumar, Satish ; Paul, Sandeep
Author_Institution :
Dept. of Phys. & Comput. Sci., Dayalbagh Educ. Inst., Agra
Abstract :
The automatic simultaneous search of structure and parameters in fuzzy-neural networks is a pressing research problem. This paper introduces a novel and powerful variable-length-crossover differential evolution algorithm, vlX-DE, which is applied to ASuPFuNIS fuzzy-neural model learning, and permits simultaneous evolution of mixed-length populations of strings representing ASuPFuNIS network instances in different rules spaces. As hybrid populations of strings evolve using vlX-DE, the population gradually converges to a single rule space after which parameter search within that space proceeds till the end of the algorithm run. Search can be directed to stress either rule node economy or minimize the sum-square-error, or trade-off between these two. Tests on three benchmark problems-iris classification, CHEM classification, and Narazaki-function approximation-clearly highlight the effectiveness of the algorithm in being able to perform this simultaneous search.
Keywords :
fuzzy neural nets; fuzzy set theory; learning (artificial intelligence); Narazaki-function approximation; automatic simultaneous architecture; fuzzy neural network learning; iris classification; variable length crossover differential evolution; Benchmark testing; Computer science; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Parameter estimation; Performance evaluation; Physics; Pressing; Stress;
Conference_Titel :
Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1818-3
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2008.4630614