Abstract A fast evolutionary programming (FEP) is proposed to train multi-layer perceptrons (MLP) for noisy chaotic time series modeling and predictions. This FEP, which uses a Cauchy mutation operator that results in a significantly faster convergence to the optimal solution, can help MLP to escape from local minima. A comparison against back-propagation-trained networks was performed. Numerical experimental results show that the FEP can help MLP better capturing dynamics from noisy chaotic time series than the back-propagation algorithm and produce a more consistently modeling and prediction.
Received: 17 January 2000
Revised: 05 March 2000
Accepted manuscript online:
Fund: Project supported by the National Defense Foundation of China (Grant No. 98JS05.4.1DZ0205).
Cite this article:
Zhang Jia-shu (张家树), Xiao Xian-ci (肖先赐) FAST EVOLVING MULTI-LAYER PERCEPTRONS FOR NOISY CHAOTIC TIME SERIES MODELING AND PREDICTIONS 2000 Chinese Physics 9 408
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