Abstract
Based on the relevance vector machine and wavelet theory, a new machine learning method–wavelet relevance vector is introduced. The method performs wavelet transform to decompose the original series into some filtered series, and a relevance vector machine whose kernel functions is wavelet function models each of them. Then, this method is used to predict fouling thermal resistance of Heat exchanger. Construction of wavelet relevance vector machine prediction model is presented. Simulations show that wavelet relevance vector machine requires dramatically fewer kernel functions and it can get high prediction precision, and work in the paper offers a new method for the research of heat exchanger fouling.
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Sun, L., Saqi, R., Xie, H. (2010). Research on the Fouling Prediction of Heat Exchanger Based on Wavelet Relevance Vector Machine. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_5
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DOI: https://doi.org/10.1007/978-3-642-12990-2_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12989-6
Online ISBN: 978-3-642-12990-2
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