基于改进人工蜂群算法的支持向量机时序预测

为使支持向量机(SVM)能更好地完成参数寻优并获得时间序列变化空间预测功能,通过改进人工蜂群社区不同蜂群的搜索方式以强化算法角色分工机制的技术优势;引入模糊信息粒化理论进一步提升支持向量机时序预测框架的学习效率、回归精度及推广能力.将方法用于上证指数时序建模并进行多角度仿真实验对比研究,无论预测精度还是泛化性能均优于现有经典方法.所提出方法具有良好的时序预测效能,对大数据背景下满意近似解及模糊性问题研究亦具有一定的启发和借鉴意义.

Time sequence prediction of SVM based on improved ABC algorithm

In order to make support vector machine(SVM)better accomplish parameters optimization and obtain forecasting fumction of time series change space,the technical advantages of algorithmic role division mechanism are strengthened by improving the search mode of different bee colonies in artificial bee colony(ABC)community, and introduce Theory of Fuzzy Information Granulation(TFIG)to further optimize the learning efficiency,regression precision and generalization ability of prediction framework. Considering that both the prediction precision and generalization performance are better than the existing classical methods when the proposed method is applied to the modeling of the SSE index.The proposed method has good time series prediction function,some inspiration and reference significance for the study of time series forecasting,satisfactory approximation and fuzziness problem in big data background.