Adaptive Prediction Model Based on Chaotic Algorithm
基于混沌算法的自适应预测模型

For a prodiction model with chaotic algorithm, the network system can escape from the local minima and converge to the global minimum or its approximate solution during the learning and prediction process by selecting the suitable nonlinnear feedback term. The dynamics of network become chaotic one in the weight space. In this work, we use the EP evolutionary computation to establish a kind of self\|suitable prediction model. The model is tested for the time series which generated with Mackey\|Glass equation and Lorentz system by on\|line method. The simulation results indicated that the network has a good self\|suitable prediction performance.