Cost Prediction of Equipment System Using LS-SVM with PSO
Considering the shortcomings of conventional cost prediction methods, least squares support vector machine (LSSVM) was adopted to establish the cost prediction model of equipment system, which could efficiently solve the problems on the determination of network structure and the phenomena of over-fitting in neural network methods. And due to the importance of parameters optimization in LS-SVM model, particle swarm optimization (PSO) was used to optimize the model parameters. The experiment results show that PSO can quickly obtain the optimal parameters satisfying the precision requirement with a simple calculation, which solves the problem of complex calculation and empiricism in conventional methods. The evaluation on the testing cases shows the LS-SVM model with PSO has a good generalization performance and can be popularized in cost prediction. At last, the experiment on an independent testing case shows the model optimized by PSO has a better prediction performance than by grid search.
least squares support vector machine particle swarm optimization cost prediction
Shangfen Guo Tiejun Jiang
Naval University of Engineering Wuhan, P.R.China
国际会议
上海
英文
2007-09-21（万方平台首次上网日期，不代表论文的发表时间）