FRULEX - Fuzzy Rules Extraction Using Rapid Back Propagation Neural Networks

In this paper, we present a new approach for extracting fuzzy rules from numerical input–output data for pattern classification. The approach combines the merits of the fuzzy logic theory, and neural networks. The proposed approach uses rapid back propagation neural network (RBPNN), which can handle both quantitative (numerical) and qualitative (linguistic) knowledge. The network can be regarded both as an adaptive fuzzy inference system with the capability of learning fuzzy rules from data, and as a connectionist architecture provided with linguistic meaning. Fuzzy rules are extracted in three phases: initialization, optimization, and simplification of the fuzzy model. In the first phase, the data set is partitioned automatically into a set of clusters based on inputsimilarity and output-similarity tests. Membership functions associated with each cluster are defined according to statistical means and variances of the data points. Then, a fuzzy if-then rule is extracted from each cluster to form a fuzzy model. In the second phase, the extracted fuzzy model is used as starting point to construct an RBPNN then the fuzzy model parameters are refined, by analyzing the nodes of the network that was trained via the back propagation gradient descent method. In the third phase, a simplification method is used to reduce the antecedent parts in the extracted fuzzy rules.