Title :
Empirical study on learning in fuzzy systems
Author :
Ishibuchi, Hisao ; Nozaki, Ken ; Tanaka, Hideo ; Hosaka, Yukio ; Matsuda, Masanori
Author_Institution :
Dept. of Ind. Eng., Univ. of Osaka Prefecture, Saiko, Osaka, Japan
Abstract :
The authors examine the ability of fuzzy systems to function as approximators of nonlinear mappings by computer simulations on real-life data. The relation among six factors in a sensory test on rice taste is modeled by fuzzy systems with five input variables and a single output variable. Fuzzy if-then rules with nonfuzzy singletons in the consequent part are employed in fuzzy systems. A learning rule based on a descent method is applied to the consequent part of each fuzzy if-then rule. By a random subsampling technique, the performance of fuzzy systems for test data and training data is compared with that of multilayer neural networks. A simple method for specifying initial fuzzy if-then rules is proposed to improve the performance of fuzzy systems
Keywords :
fuzzy logic; learning (artificial intelligence); approximators; descent method; fuzzy if-then rule; fuzzy systems; learning rule; nonlinear mappings; random subsampling technique; rice taste; Automatic control; Computer simulation; Fuzzy control; Fuzzy systems; Industrial engineering; Input variables; Multi-layer neural network; Qualifications; System testing; Training data;
Conference_Titel :
Fuzzy Systems, 1993., Second IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0614-7
DOI :
10.1109/FUZZY.1993.327419