基于近似熵测度的变权组合预测方法  

Image registration algorithm based on intuitionistic fuzzy distance

作　　者：徐宇亮[1] 陈西宏[1] 马超[1] 王光明[1] 

机构地区：[1]空军工程大学防空反导学院,西安710051

出　　处：《控制与决策》2013年第1期115-119,共5页Control and Decision

基　　金：国家自然科学基金项目(60971118)

摘　　要：提出一种基于近似熵测度的变权组合预测方法.首先,不同于传统的预测效果评价准则,从衡量样本序列复杂性的角度出发,以预测值误差序列的近似熵测度为评价效果准则,建立变权组合预测优化模型;然后,在变权组合预测权值分配问题上,为克服常规的均值估计法和回归分析法的不足,采用在线最小二乘支持向量机(LS-SVM)回归法,实现预测点加权系数的准确预测;最后,通过实例表明了该方法的可行性和有效性.A variable weight combined forecasting method based on approximate entropy is proposed. Firstly, unlike the traditional evaluation criterion, and considering measurement complexity of sequential sample, an optimizing model of variable weight combined forecasting is established according to the approximate entropy of the prediction error sequential. Then, the weight allocation problem is considered. To avoid the insufficiency of the conventional method （e.g. mean estimation and regression analysis）, online least squares support vector machine（LS-SVM） regression method is used to achieve accurate forecasting about weight. Finally, an example shows the feasibility and effectiveness of the proposed method.

关 键 词：近似熵测度 变权组合预测 在线LS-SVM 故障预测 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]