摘要
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。
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 predic- tion 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.
出处
《中山大学学报（自然科学版）》
CAS
CSCD
北大核心
2012年第5期113-119,共7页
Acta Scientiarum Naturalium Universitatis Sunyatseni
基金
国家自然科学基金资助项目(41065002
11061008)
广西科学攻关基金资助项目(桂科攻0993002-4)
广西教育厅科研基金资助项目(200911MS151)
关键词
粒子寻踪
遗传算法
神经网络
预测模型
pursuit of particle swarm
genetic algorithm
neural networks
prediction model
