title: A new learning algorithm for a fully connected neuro-fuzzy inference system.
abstract: A traditional neuro-fuzzy system is transformed into an equivalent fully connected three layer neural network (NN), namely, the fully connected neuro-fuzzy inference systems (F-CONFIS). The F-CONFIS differs from traditional NNs by its dependent and repeated weights between input and hidden layers and can be considered as the variation of a kind of multilayer NN. Therefore, an efficient learning algorithm for the F-CONFIS to cope these repeated weights is derived. Furthermore, a dynamic learning rate is proposed for neuro-fuzzy systems via F-CONFIS where both premise (hidden) and consequent portions are considered. Several simulation results indicate that the proposed approach achieves much better accuracy and fast convergence.
authors.forename: C L Philip
authors.initials: CL
authors.lastname: Chen
authors.forename: Jing
authors.initials: J
authors.lastname: Wang
authors.forename: Chi-Hsu
authors.initials: CH
authors.lastname: Wang
authors.forename: Long
authors.initials: L
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D017143:Fuzzy Logic; D006801:Humans; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D018511:Systems Integration