A pseudo outer-product based fuzzy neural network and its rule-identification algorithm

A novel fuzzy neural network, called the pseudo outer-product based fuzzy neural network (POPFNN), is proposed in this paper. Similar to most existing fuzzy neural networks, the proposed POPFNN uses a self-organizing algorithm to learn and initialize the membership functions of the input and output variables from a set of training data. However, instead of employing the commonly used competitive learning, we proposed a novel one-pass lazy pseudo outer-product (LazyPOP) learning algorithm to identify the fuzzy rules that are supported by the training data. In contrast with other rule-identification algorithms the proposed LazyPOP learning algorithm is fast, reliable, and highly intuitive. Extensive experimental results and comparisons are presented.