The Optimization of Plastic Injection Molding Process Based on Support Vector Machine and Genetic Algorithm
暂无分享,去创建一个
The paper presents the radial basis kernel parameters of the support vector machine (SVM) regression model employed to determine the complex and nonlinear relationships between the injection molding parameters and the defects of plastic injection molded parts, whereas genetic algorithm (GA) is applied to determine a set of optimal nuclear parameters for SVM. Then, an approximate analysis model is established, and it is proved effective by numerical examples of the plastic injection molded parts. All these explored an effective method of numerical simulation model for optimization of the plastic injection molding process.
[1] Lijuan Cao,et al. Support vector machines experts for time series forecasting , 2003, Neurocomputing.
[2] Ping-Feng Pai,et al. Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms , 2005 .
[3] Ping-Feng Pai,et al. Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting , 2005 .
[4] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.