基于粒子群-投影寻踪和遗传-神经网络集成的预测模型

  针对预测对象和预测因子存在复杂的线性和非线性关系的特点，利用自然正交展开方法进行线性降维，以及用粒子群 投影寻踪方法进行非线性降维，将高维的非线性数据投影到低维子空间上，构造了一种遗传 神经网络预测模型。在此基础上，应用该预测模型对影响华南的台风频数进行了预测试验，并将预测结果与统计回归模型的预测结果进行对比分析。结果表明，文中构建的非线性集预测模型，对台风频数有较好的预测效果，5 年预测的平均绝对误差为0.81个, 平均相对误差为13%，预测结果比统计回归模型有明显的改进。该文的结果可为进一步探索研究其他领域的预测建模提供了一种新的参考思路和方法。

Prediction Model based on Particle Swarm-projection Pursuit and Genetic-neural Networks

Accurate prediction models are expected for many disciplines. Considering the complicated linear and nonlinear relations among forecast objects and predictive factors, the natural orthogonal complement method and the projection pursuit of particle swarm optimization algorithm are used for the linear dimensional reduction and the nonlinear dimensional reduction, respectively. With this procedure, we project the high-dimensional nonlinear data to low-dimensional subspace and construct a genetic-neural networks integrated prediction model. The model is tested in the frequency prediction of landing-typhoon in southern China and then the model accuracy is compared with the result obtained by the regular regression statistical prediction method. The mean absolute error and the mean relative error of the five-year test prediction for the typhoon frequency are 0.81 and 13%, respectively, by using the new nonlinear prediction model proposed in this paper. The prediction results by the new model have been obviously improved, comparing to regular regression statistical prediction method. The results provide a new thinking and method for the prediction model study in other disciplines.