Rice Taste Analysis by Trainable Fuzzy If-Then Rules

Abstract

This paper constructs fuzzy systems based on trainable fuzzy if-then rules for rice taste analysis and examines the ability of fuzzy systems by computer simulations. The relation between six factors in the sensory test on rice taste is modelled by fuzzy systems with five input variables(flavor, appearance, taste, stickiness, toughness)and a single output variable(overall evaluation). Fuzzy if-then rules with non-fuzzy singletons in the consequent parts 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 multi-layer neural networks. The usefulness of generated fuzzy if-then rules by learning is also examined.