Name
Title | ||
|---|---|---|
Reliable Prediction System Based on Support Vector Regression with Genetic Algorithms |
Abstract | ||
|---|---|---|
This study applies a novel neural-network technique, support vector regression (SVR), to predict reliably in dynamical system. The aim of this study is to examine the feasibility of SVR in state prediction by comparing it with the existing neural-network approaches. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVR's optimal parameters using genetic algorithms, and then adopts the optimal parameters to construct the SVR models. The application results of practical vibration data state forecasting measured from a Co2 compressor demonstrate that the GA-SVR model outperforms the existing neural network based on the criteria of mean absolute error (MAE) and root mean square error (RMSE). |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICNC.2009.176 | ICNC (1) |
Keywords | Field | DocType |
time series prediction,novel neural-network technique,mean absolute error,svr,state prediction,support vector regression,regression analysis,genetic algorithms,vibration,reliable prediction system,existing neural-network approach,svr model,square error,existing neural network,genetic algorithm,prediction system reliability,effective svr model,neural-network approaches,root mean square error,optimal parameter,novel approach,support vector machines,neural nets,mean square error methods,ga-svr model,gallium,predictive models,neural network,forecasting,dynamic system,vibrations | Time series,Data mining,State prediction,Regression analysis,Computer science,Support vector machine,Mean squared error,Artificial intelligence,Artificial neural network,Genetic algorithm,Dynamical system,Machine learning | Conference |
Volume | ISBN | Citations |
1 | 978-0-7695-3736-8 | 1 |
PageRank | References | Authors |
0.36 | 7 | 3 |