Research on the Fouling Prediction Based on Hybrid Kernel Function Relevance Vector Machine
The research on the fouling prediction of heat exchanger is significantly to improve operational efficiency and economic benefits of the plants. Based on the relevance vector machine with Gaussian kernel function, polynomial kernel function and hybrid kernel function, simulation research on the fouling prediction was introduced. We construct a six-inputs and one-output network model according to the fouling monitor principle and parameters with MATLAB, all training data came from the Automatic Dynamic Simulator of Fouling and input the network after normalized processing and reclassification. Simulations show that the root mean square error of fouling prediction with hybrid kernel function is less than simple kernel function, and has the better prediction precision.
Helen Zhang, Gang Shen and David Jin
L. F. Sun et al., "Research on the Fouling Prediction Based on Hybrid Kernel Function Relevance Vector Machine", Advanced Materials Research, Vols. 204-210, pp. 31-35, 2011