关键词: 克隆规划, 交叉验证, 参数优化, 最小二乘支持向量机, 惯性器件预测

Abstract:

For improving the generalization ability of the least square support vector machine (LSSVM), the parameter optimization algorithm of clone programming-cross validation is employed to select optimal parameters of LSSVM. The clone programming algorithm has the superior capability in local and global search, and local minimums are refrained efficiently; cross validation has the unbiased estimator property, and therefore, the problems such as over training or insufficient training are avoided. In the optimization algorithm, the avidity function is constructed by the cross validation error, and moreover, optimal parameters of LSSVM are chosen by the clone programming algorithm. The time series forecasting model of the inertial component is built with LSSVM. Experimental results prove the effectiveness of the optimization algorithm and generalization ability of the forecasting model, and the forecasting model provides a support on dynamic compensation and fault forecasting of the inertial component.

Key words: clone programing, cross validation, parameters optimization, least square support vector machine, inertial component forecasting