Learning Fuzzy Rules from Data

Abstract

We present an algorithm, FUZZEX, for learning fuzzy rules from a corpus of data mapping input antecedents to output consequents. The input and output spaces are first divided into a grid of cells and primitive if % then rules formulated from each occupied input cell in the role of an antecedent. The distribution of output cells into which data in the input cell maps, plays the role of the consequent interpreted as a fuzzy set. Those input cells associated with sufficiently similar fuzzy output sets are then combined to form a composite rule. A concise set of rules in Disjunctive Normal Form (DNF) is formed by combining adjacent input cells belongingto the same rule, thereby simplifying the logical expression of the antecedents. Optionally, more succinctness of expression may be obtained by recruiting into a rule, adjacent input cells with (little or) no data, but which happen to simplify rule expression. Preliminary testing on testbed datasets is presented. FUZZEX can be applied effectively to problems of large dimensionality.

Flow Discharge Modeling in Open Canals Using a New Fuzzy Modeling Technique (SMRGT)

“…Casillas et al introduced a novel approach to the fuzzy rule learning problem with ant colony optimization (ACO) algorithms [79]. Finn presented an algorithm, FUZ-ZEX, for learning FRs from a corpus of data mapping input antecedents to output consequents [80]. Chen and Lee proposed another method to construct fuzzy decision trees from relational database systems and to generate fuzzy rules from the constructed fuzzy decision trees for estimating null values, where the weights of attributes are used to derive the values of certainty factors of the generated FRs [81].…”

Creating Music With Fuzzy Logic

“…However, fuzzy rules can also be learned from data (Kosko, 1992). One example is the FUZZEX algorithm which can learn rules from a corpus of data mapping inputs to outputs, in the same fashion that a neural network does (Finn, 1999). Formally, a fuzzy rule is a conditional of the form IF X is A THEN Y is B, where A and B are fuzzy sets (Kosko, 1993).…”

“…However, fuzzy rules can also be learned from data (Kosko, 1992 ). One example is the algorithm which can learn rules from a corpus of data mapping inputs to outputs, in the same fashion that a neural network does (Finn, 1999 ).…”