EMPIRICAL-STUDY ON LEARNING IN FUZZY-SYSTEMS BY RICE TASTE ANALYSIS

The aim of this paper is to examine the ability of trainable fuzzy sys tems as approximators of non-linear mappings by computer simulations o n real-life data. Fuzzy if-then rules with non-fuzzy singletons in the consequent part are adjusted by a gradient descent method in fuzzy sy stems. After examining the ability of fuzzy systems for numerical exam ples, we apply them to the modelling of the relation among six factors in the sensory test on rice taste. By computer simulations based on a random subsampling technique, it is shown that the performance of fuz zy systems is comparable to that of neural networks. It is also shown that pre-specified conditions such as a fuzzy partition, initial fuzzy if-then rules and the number of iterations have a significant effect on the performance of trained fuzzy systems.