Chaotic Time Series Prediction Algorithm for Lorenz System

Chen Zhang

doi:10.4028/www.scientific.net/AMM.513-517.2412

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 513-517Chaotic Time Series Prediction Algorithm for...

Chaotic Time Series Prediction Algorithm for Lorenz System

Abstract:

Based on the glowworm swarm optimization (GSO) and BP neural network (BPNN), an algorithm for BP neural network optimized glowworm swarm optimization (GSOBPNN) is proposed. In the algorithm, GSO is used to generate better network initial thresholds and weights so as to compensate the random defects for the thresholds and weights 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 generated by Lorenz system. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so prove it is feasible and effective in the chaotic time series.