A Novel Recurrent Interval Type-2 Fuzzy Neural Network for Nonlinear Channel Equalization
Ching Hung Lee, Hao Chang, Che Ting Kuo, Jen Chieh Chien, Tzu Wei Hu · 2009
Abstract—In this paper, we propose a novel recurrent interval type-2 fuzzy neural network with asymmetric membership functions (RT2FNN-A). The RT2FNN-A uses the interval asymmetric type-2 fuzzy sets and it implements the fuzzy logic system (FLS) in a five-layer neural network structure. The RT2FNN-A is modified from the type-2 fuzzy neural network to provide memory elements for capturing the system’s dynamic information and has the properties of high approximation accuracy and small network structure. Based on the Lyapunov theorem and gradient descent method, the convergence of RT2FNN-A is guaranteed and the corresponding learning algorithm is derived. In addition, the RT2FNN-A is applied in the nonlinear channel equalization to show the performance and effectiveness of RT2FNN-A system. Index Terms—type-2 fuzzy logic system, recurrent neural network, asymmetric membership functions, channel equalization I.