Ensemble Neural Network Optimization Using the Particle Swarm Algorithm with Type-1 and Type-2 Fuzzy Integration for Time Series Prediction

2014 ◽  
pp. 99-112
Author(s):  
Martha Pulido ◽  
Patricia Melin
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Optimization ◽  
Time Series Prediction ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Neural Network Optimization ◽  
Ensemble Neural Network

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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