混沌经济时序非线性动力系统的预测方法研究

混沌经济时序的预测方法研究是混沌经济非线性动力系统的重要内容，提出了一种改进的非线性混沌经济模型，应用模型的阶数由最小嵌入维数决定的观点，给出了相应的模型参数辨识方法，并用其确立的混沌模型进行了预测工作，计算结果表明：模型参数辨识方法能迅速地将参数估计值带到多峰目标函数的全局最小值附近，然后再采用优化理论能较准确地求出模型的参数，用得到的混沌模型对系统进行预测工作，其短期预测效果良好。

Prediction in chaotic nonlinear dynamic systems: method for economic experimental data time series

The non-linear chaotic model reconstruction is the major important quantitative index for describing accurate experimental data obtained in dynamic analysis. This paper presents a new improved EAR(n) method in modelling and predicting chaotic timeseries, and proposes a successful approach to fast estimation algorithms . Some illustrative experimental data examples from known chaotic systems are presented, emphasising the increase in prediction error with time.The calculating results tell us that the parameter identification method in this paper can effectively adjust the initial value towards the global limit value of the single peak target function nearby. Then we can immediately obtain the model parameter by using the improved optimization method rapidly, and nonlinear chaotic models can not provide long period superior predictions. We list applications of this method to real data from widely different areas.