Abstract
The aim of this paper is to examine the ability of trainable fuzzy systems as approximators of non-linear mappings by computer simulations on real-life data. Fuzzy if-then rules with non-fuzzy singletons in the consequent part are adjusted by a gradient descent method in fuzzy systems. After examining the ability of fuzzy systems for numerical examples, 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 fuzzy 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.
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