Abstract
Based on the character and requirement of the dynamic weighing of loader, the soft sensor technique was adapted as the weighing method, and the least square support vector machine (LS-SVM) as its modelling method. Also the Bayesian evidence framework was used in LS-SVM for selecting and tuning its parameter. And then, after the nonlinear regression algorithms of LS-SVM and the principle of Bayesian evidence framework were introduced, the soft sensor model based on LS-SVM was given. In the end, emulation analysis results indicate that soft-sensor method based on LS-SVM within Bayesian evidence framework is a valid means for solving dynamic weighing of loader.
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References
Huang, F.L.: Soft-Sensing Idea and Soft-Sensing Techniques. Acta Metrologica Sinca 25, 284–286 (2004)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Suykens, J.A.K., Vandewalle, J.: Least Square Support Vector Machines for Classification and Nonlinear Modelling. Neural Network World 10, 29–48 (2000)
Suykens, J.A.K., Vandewalle, J.: The Least Square Support Vector Machines Classifiers. Neural Network Letters 19, 293–300 (1999)
Yan, W.W., Shao, H.H., et al.: Soft Sensor Modelling Based on Support Vector Machine and Bayesian Model Selection. Computing and Chemical Engineering 28, 1489–1498 (2004)
Gestel, T.V., Suykens, J.A.K., et al.: Financial Time Series Prediction using Least Squares Support Vector Machines within the Evidence Framework. IEEE Transactions on Neural Network 12, 809–820 (2001)
Kowk, J.T.: The Evidence Framework Applied to Support Vector Machines. IEEE Transactions on Neural Network 11, 1162–1173 (2000)
Law, M.H.C., Kwok, J.T.: Applying the Bayesian Evidence Framework to ν-Support Vector Regression. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 312–323. Springer, Heidelberg (2001)
Müller, K.R., Smola, A.J., et al.: Predicting Time Series with Support Vector Machines. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 999–1004. Springer, Heidelberg (1997)
Mackay, D.J.C.: Probable Networks and Plausible Predictions-A Review of Practical Bayesian Methods for Supervised Neural Networks. Network computation in Neural Systems 6, 469–505 (1995)
Gestel, T.V., Espinoza, M., et al.: A Bayesian Nonlinear Support Vector Machine Error Correction Model. Journal of forecasting 25, 77–100 (2006)
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Wang, W., Wang, T., Wei, H., Zhao, H. (2007). Soft-Sensor Method Based on Least Square Support Vector Machines Within Bayesian Evidence Framework. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_67
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DOI: https://doi.org/10.1007/978-3-540-72395-0_67
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