Nonlinear Time Series Prediction Based on Lyapunov Theory-Based Fuzzy Neural Network and Multiobjective Genetic Algorithm

Abstract

Abstract. This paper presents the nonlinear time series prediction using Lyapunov theory-based fuzzy neural network and multi-objective ge-netic algorithm (MOGA). The architecture employs fuzzy neural net-work (FNN) structure and the tuning of the parameters of FNN using the combination of the MOGA and the modified Lyapunov theory-based adaptive filtering algorithm (LAF). The proposed scheme has been used for a wide range of applications in the domain of time series prediction. An application example on sunspot prediction is given to show the mer-its of the proposed scheme. Simulation results not only demonstrate the advantage of the neuro-fuzzy approach but it also highlights the advan-tages of the fusion of MOGA and the modified LAF.