时空混沌序列的局域支持向量机预测

结合局域预测法计算速度快的优点和支持向量机的泛化性能好、全局最优、稀疏解等特性，用局域支持向量机预测研究了时空混沌序列的局域预测性能，并用局域支持向量机预测模型讨论了嵌入维数、邻近个数选择以及时空混沌的耦合方式和格子间的耦合强度变化对时空混沌局域预测性能的影响.研究结果表明：局域支持向量机不仅比全局支持向量机、局域零阶预测、局域线性预测等方法具有更好的预测性能，且具有对嵌入维数和邻近个数不敏感的优点；时空混沌的耦合方式和格子间的耦合强度对时空混沌序列的预测性能有明显影响.

Local support vector machine prediction of spatiotemporal chaotic time series

In this paper, local support vector machine (LSVM), which combins the advantage of traditional local prediction methods and support vector machines, is proposed to make local predictions of spatiotemporal time series. The LSVM is also used to discuss the selection of embedding dimension and the number of nearest neighbours, the coupling-way and the coupling coefficients of spatiotemporal chaotic systems that influence on the local predictions of spatiotemporal chaotic time series. Experimental results show that the LSVM can not only make better predictions of spatiotemporal chaotic time series than that of local zero-order methods and local linear methods and global support vector machine, but the computational complexity can also be reduced greatly compared to the global support vector machine. Moreover, the LSVM is insensitive to the selection of embedding dimension and the number of nearest neighbours. In addition, the local prediction performance of spatiotemporal chaotic time series is influenced by the coupling-way and the coupling coefficients of spatiotemporal chaotic systems.