TY - GEN
T1 - Neural networks and genetic algorithm approaches to auto-design of fuzzy systems
AU - Takagi, Hideyuki
AU - Lee, Michael
N1 - Funding Information:
There are three major design decisions to make when designing fuzzy systems: (1) deciding the number of fuzzy rules, (2) deciding the shape of the membership functions, (3) deciding the consequent parameters. Furthermore, two other decisions must be made: (4) deciding the number of input variables, (5) deciding the reasoning method. (1) and (2) correspond to deciding how to cover the input space. They are highly dependent on each other. (3) corresponds to determining the coefficients of the linear equation in the case of the TSK (Takagi-Sugeno-Kang) model [1], 0 This research is supported in part by NASA Grant NCC-2-275, MICRO State Program Award No.90-191, and EPRI Agreement RP8010-34. We would like to thank Prof. David Wessel and the Center for New Music and Audio Technologies at UC Berkeley for use of computing resources.
Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1993.
PY - 1993
Y1 - 1993
N2 - This paper presents Neural Network and Genetic Algorithm approaches to fuzzy system design, which aims to shorten development time and increase system performance. An approach that uses neural network to represent multi-dimensional nonlinear membership functions and an approach to tune membership function parameters are given. A genetic algorithm approach that integrates and automates three fuzzy system design stages is also proposed.
AB - This paper presents Neural Network and Genetic Algorithm approaches to fuzzy system design, which aims to shorten development time and increase system performance. An approach that uses neural network to represent multi-dimensional nonlinear membership functions and an approach to tune membership function parameters are given. A genetic algorithm approach that integrates and automates three fuzzy system design stages is also proposed.
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U2 - 10.1007/3-540-56920-0_9
DO - 10.1007/3-540-56920-0_9
M3 - Conference contribution
AN - SCOPUS:84948161800
SN - 9783540569206
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 68
EP - 79
BT - Fuzzy Logic in Artificial Intelligence - 8th Austrian Artificial Intelligence Conference, FLAI 1993, Proceedings
A2 - Klement, Erich P.
A2 - Slany, Wolfgang
PB - Springer Verlag
T2 - 8th Austrian Artificial Intelligence Conference on Fuzzy Logic, FLAI 1993
Y2 - 28 June 1993 through 30 June 1993
ER -