title: Simplified interval type-2 fuzzy neural networks.
abstract: This paper describes a self-evolving interval type-2 fuzzy neural network (FNN) for various applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information within the knowledge base, we propose a simple interval type-2 FNN, which uses interval type-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang (TSK) type in the consequent of the fuzzy rule. The TSK-type consequent of fuzzy rule is a linear combination of exogenous input variables. Given an initially empty the rule-base, all rules are generated with on-line type-2 fuzzy clustering. Instead of the time-consuming K-M iterative procedure, the design factors ql and qr are learned to adaptively adjust the upper and lower positions on the left and right limit outputs, using the parameter update rule based on a gradient descent algorithm. Simulation results demonstrate that our approach yields fewer test errors and less computational complexity than other type-2 FNNs.
authors.forename: Yang-Yin
authors.initials: YY
authors.lastname: Lin
authors.forename: Shih-Hui
authors.initials: SH
authors.lastname: Liao
authors.forename: Jyh-Yeong
authors.initials: JY
authors.lastname: Chang
authors.forename: Chin-Teng
authors.initials: CT
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D017143:Fuzzy Logic; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer; D013997:Time Factors