Nonlinear time series prediction based on lyapunov theory-based fuzzy neural network and multiobjective genetic algorithm

Kah Phooi Seng, Kai Ming Tse

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationAI 2003
Subtitle of host publicationAdvances in Artificial Intelligence - 16th Australian Conference on AI, Proceedings
EditorsTamas D. Gedeon, Lance Chun Che Fung, Tamas D. Gedeon
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783540206460
Publication statusPublished - 2003
Externally publishedYes
Event16th Australian Conference on Artificial Intelligence, AI 2003 - Perth, Australia
Duration: 3 Dec 20035 Dec 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th Australian Conference on Artificial Intelligence, AI 2003

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