Evolutionary Neural Networks for Time Series Prediction
Yung-Chin Lin, Yung-Chien Lin, Kuo-Lan Su · 2010
A novel application to the optimization of neural networks is presented in this paper. Here, the weight and architecture optimization of neural networks can be formulated as a mixed-integer optimization problem. And then a mixed-integer evolutionary algorithm (Mixed-Integer Hybrid Differential Evolution, MIHDE) is used to optimize the neural network. Finally, the optimized neural network is applied to the prediction of chaotic time series. The satisfactory results are achieved, and demonstrate that the neural network optimized by MIHDE can effectively predict the chaotic time series.