A pseudo outer-product based fuzzy neural network
R.W. Zhou, Chai Hiok Quek · 2002
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, the authors proposed a novel one-pass pseudo outer-product (POP) learning algorithm to identify the fuzzy rules that are supported by the training data. In contrast with other rule-identification algorithms, the proposed POP learning algorithm is fast, reliable, and highly intuitive. Extensive experimental results and comparisons are presented at the end of the paper.