Regularized Fuzzy Neural Networks for Pattern Classification Problems

摘要

This paper presents a novel learning algorithm for fuzzy logic neuron based networks able to generate accurate and transparent models. The learning algorithm is based on ideas from Extreme Learning Machine, to achieve a low time complexity, and regularization theory, resulting in sparse and accurate models. A compact set of incomplete fuzzy rules can be extracted from the resulting network topology. Experiments considering pattern classification are detailed. Results suggest the proposed approach as a promising alternative for pattern recognition with a good accuracy and some level of interpretability.