Temperature Prediction of Hydrogen Producing Reactor Using SVM Regression with PSO
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| Title | Temperature Prediction of Hydrogen Producing Reactor Using SVM Regression with PSO |
| Authors | |
| Abstract | Temperature forecasting of hydrogen-producing reactor is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. The determination for hyper-parameters including kernel parameters and the regularization is important to the performance of SVM. Particle Swarm Optimization (PSO) is a method for finding a solution of stochastic global optimizer based on swarm intelligence. Using the interaction of particles, PSO searches the solution space intelligently and finds out the best one. Thus, the proposed forecasting model based on the global optimization of PSO and local accurate searching of SVM is applied to forecast hydrogen-producing reactor temperature in this paper. Practical example results indicate that the application of the PSO-SVM method to temperature forecasting of hydrogen-producing reactor is feasible and effective. And to prove the effectiveness of the model, other existing methods are used to compare with the result of SVM. The results show that the model is effective and highly accurate in the forecasting of hydrogen-producing reactor temperature. |
| Publisher | ACADEMY PUBLISHER |
| Date | 2010-03-01 |
| Source | Journal of Computers Vol 5, No 3 (2010) |
| Rights | Copyright © ACADEMY PUBLISHER - All Rights Reserved.To request permission, please check out URL: http://www.academypublisher.com/copyrightpermission.html. |