A new approach for chaotic time series prediction using recurrent neural network
type: Item
instance of: scholarly article
title: A new approach for chaotic time series prediction using recurrent neural network
zbMATH Open document ID: 1400.62215
DOI: 10.1155/2016/3542898
author: Qinghai Li
author: R. C. Lin
published in: Mathematical Problems in Engineering
publication date: 2018-10-12T00:00:00Z
review text: Summary: A self-constructing fuzzy neural network (SCFNN) has been successfully used for chaotic time series prediction in the literature. In this paper, we propose the strategy of adding a recurrent path in each node of the hidden layer of SCFNN, resulting in a self-constructing recurrent fuzzy neural network (SCRFNN). This novel network does not increase complexity in fuzzy inference or learning process. Specifically, the structure learning is based on partition of the input space, and the parameter learning is based on the supervised gradient descent method using a delta adaptation law. This novel network can also be applied for chaotic time series prediction including Logistic and Henon time series. More significantly, it features rapider convergence and higher prediction accuracy.
Mathematics Subject Classification ID: 62M45
Mathematics Subject Classification ID: 62M86
zbMATH DE Number: 6953067
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/3542898
OpenAlex ID: W2560817176
Wikidata QID: Q59130880
cites work: Nonlinear prediction of chaotic time series
cites work: Circulant Synthesis of Central Pattern Generators With Application to Control of Rectifier Systems
cites work: Advances in Neural Networks – ISNN 2005
cites work: Matrix perturbation analysis for weakly coupled oscillators
cites work: Neural control for coordinated natural oscillation patterns
cites work: Q4346204
cites work: A two-dimensional mapping with a strange attractor
description: scientific article; zbMATH DE number 6953067