基于优化最小二乘支持向量机的小样本预测研究

Data Prediction with Few Observations Based on Optimized Least Squares Support Vector Machines

统计学中的预测问题主要是通过对已知数据的分析,找到数据内在的相互依赖关系,从而获得对未知数据的预测能力。该文提出了最小二乘支持向量机参数优化方法———多层动态自适应优化算法,构建了基于最小二乘支持向量机的预测模型,并对Ti 26合金的性能预测进行了研究。结果表明:优化的最小二乘支持向量机具有优秀的小样本数据学习能力和预测能力。

In traditional statistical methods, large samples are needed for accurate function estimation and data prediction. Least squares support vector machines (LS-SVM's) is a machine learning method for function estimation even with small samples. However, inappropriate LS-SVM's algorithmic parameters always bring poor results. A LS-SVM's algorithmic parameters optimization method is suggested which is called multilayer adaptive best-fitting parameters search algorithm. Learning error of samples can be controlled to minimum by the method. And then, a data prediction model based on the parameter-optimized LS-SVM's is approached, and the Ti-26 alloy material performance prediction experiments are analyzed with this model. The results show that the model has excellent learning ability and generalization and can provide more accurate data prediction only with fewer observed samples, as compared with supervised linear feature mapping neural network.