Paper Info
Title
Nonlinear Time Series Prediction Based on Lyapunov Theory-Based Fuzzy Neural Network and Multiobjective Genetic Algorithm
Abstract
This paper presents the nonlinear time series prediction using Lyapunov theory-based fuzzy neural network and multi-objective genetic algorithm (MOGA). The architecture employs fuzzy neural network (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 merits of the proposed scheme. Simulation results not only demonstrate the advantage of the neuro-fuzzy approach but it also highlights the advantages of the fusion of MOGA and the modified LAF.
Year
DOI
Venue
2003
10.1007/978-3-540-24581-0_76
AI 2003: ADVANCES IN ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
fuzzy neural network,time series prediction,neuro fuzzy,adaptive filter
Lyapunov function,Control theory,Computer science,Fuzzy logic,Algorithm,Adaptive filter,Fuzzy control system,Adaptive algorithm,Artificial neural network,Genetic algorithm,Fuzzy rule
Conference
Volume
ISSN
Citations 
2903
0302-9743
0
PageRank 
References 
Authors
0.34
4
2
Name
Order
Citations
PageRank
Kah Phooi Seng126032.70
Kai-ming Tse200.68