A new fast-F-CONFIS training of fully-connected neuro-fuzzy inference system

摘要

In this paper, Fuzzy Neural Network (FNN) is transformed into an equivalent Fully Connected Neuro-Fuzzy Inference System (F-CONFIS). The F-CONFIS is a new type of neural network that differs from traditional neural networks, which there are the dependent and repeated weights. For these special properties, its learning algorithm should be different from that of the conventional neural networks. Therefore, a new efficient training algorithm for F-CONFIS is proposed. Simulation examples are given to verify the validity of the proposed method, and achieve satisfactory results. In all engineering applications using FNN, developing Fast-F-CONFIS training has its emerging values.