遗传算法在经济混沌组合预测中的应用

经济混沌是由确定性的经济系统产生具有随机性的动态行为。虽然经济系统产生的时间序列不具有长期预测性，但在短期内可有较为精确的预测，可以为其建立确定性的预测模型。为了探求经济时间序列中的混沌特性，文中在Wolf提出的相空间重构的基础上，提出了一种基于遗传算法的经济混沌组合预测方法，在该方法中采用自适应并行遗传算法确定组合模型中权系数，这样可以较好地解决传统经济混沌预测大多数都是使用单一模型以至影响了观测精度等问题，最后以实例检验了提出的算法。

The Genetic Algorithms Applied in the Combination Forecasting of Economic Chaos

Economic chaos is a random-like dynamic behavior resulting from definitive economic system . Although the time series from economic system isn't forecasted in a long term ,but can be predicted precisely in a short term , thus , by which a deterministic model can be constructed . To search chaos character in economic time series , we present a combined forecasting method of economic chaos basing on genetic algorithms and the reconstruction of phase space given by Wolf, this method takes advantage of genetic algorithms to determine the weighting coefficient of combined forecasting. It's greatly overcome the defaults that when traditional forecasting of economic chaos single model is mostly used so as to affect the forecasting precision. At last , an example is given to testify the validity and feasibility of this method.