A Chaotic Time Series Prediction Method Based on Fuzzy Neural Network and Its Application

2010 ◽  
Author(s):  
Zhuo Chen ◽  
Chen Lu ◽  
Wenjin Zhang ◽  
Xiaowei Du
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fuzzy Neural Network ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Chaotic Time Series ◽  
Fuzzy Neural ◽  
Chaotic Time Series Prediction

scholarly journals A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Qinghai Li ◽  
Rui-Chang Lin
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Descent Method ◽  
Chaotic Time Series ◽  
Gradient Descent Method ◽  
Fuzzy Neural ◽  
Chaotic Time Series Prediction

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.

Study on Chaotic Time Series Prediction Algorithm for Kent Mapping Based on Particle Swarm Optimization

2014 ◽  
Vol 511-512 ◽  
pp. 941-944 ◽  
Author(s):  
Hong Li Bian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Chaotic Time Series ◽  
Learning Ability ◽  
Swarm Optimization ◽  
Chaotic Time Series Prediction

Based on the particle swarm optimization (PSO) and BP neural network (BPNN), an algorithm for BP neural network optimized particle swarm optimization (PSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight to compensate the defect of connection weight and thresholds of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series for Kent mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series prediction.

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