A novel single multiplicative neuron model trained by an improved glowworm swarm optimization algorithm for time series prediction

2015 ◽  
Vol 88 ◽  
pp. 195-209 ◽  
Author(s):  
Huimin Cui ◽  
Jianxin Feng ◽  
Jin Guo ◽  
Tingfeng Wang
Keyword(s):  
Time Series ◽  
Optimization Algorithm ◽  
Neuron Model ◽  
Time Series Prediction ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization

Chaotic Time Series Prediction for Tent Mapping Based on BP Neural Network Optimized Glowworm Swarm Optimization

2014 ◽  
Vol 513-517 ◽  
pp. 1096-1100
Author(s):  
Yue Hou ◽  
Hai Yan Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Chaotic Time Series ◽  
Learning Ability ◽  
Connection Weight ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization

In order to improve the neural network structure and setting method of parameters, based on the glowworm swarm optimization (GSO) and BP neural network (BPNN), an algorithm of BP neural network optimized glowworm swarm optimization (GSOBPNN) is proposed. In the algorithm, GSO is used to obtain better network initial threshold and weight so as to compensate the defect of connection weight and thresholds choosing of BPNN, thus BPNN can have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series of tent 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.

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