Genetic fuzzy learning
A hybrid approach to fuzzy supervised learning is presented. It is based on a genetic-neuro learning algorithm. The mixed-genetic coding adopted involves only the premises of the fuzzy rules. The conclusions are derived through a least-squares solution of an over-determined system using the singular value decomposition (SVD) algorithm, The paper presents the results obtained with C++ software called GEFREX that implements the proposed algorithm. The main characteristic of the algorithm is the compactness of the fuzzy systems extracted. Several comparisons ranging from approximation problems, classification problems, and time series predictions show that GEFREX reaches a smaller error than found in previous works With the same or a smaller number of rules, Further, it succeeds in identifying significant features. Although the SVD is used extensively, the learning time is decidedly reduced in comparison with previous work.
singular value decomposition time series prediction fuzzy logic genetic algorithms machine learning neural networks