Study On Kernel Function Of Relevance Vector Machine And Its Application In Wastewater System

Wastewater treatment is becoming more and more complex and has the characteristics of parameter time-varying, multi-variable, strong coupling, nonlinear, and serious lag, etc. In face of these characteristics, important variables can’t be measured by traditional method in an accurate, environment-friendly and economical way, resulting in that sewage treatment quality is difficult to be guaranteed and cost much. In order to solve this problem, it is necessary to establish economical and environmentally friendly sewage soft measurement model which has high predictor accuracy. Soft measurement technology is a new intelligent detection technology. Based on wastewater treatment process and the advantages of Relevance Vector Machine and the analysis of kernel function, this paper proposes a kind of soft measurement model by combining Multi-attributes Gaussian kernel with Relevance Vector Machine, and the proposed model is successfully used to predict the sewage system effluent parameters. Main content of this paper is as follows:First, study the principle of Relevance Vector Machine, and focus on analysis of its convergence on the basis of EM iterative estimation. In view of the fact that Relevance Vector Machine wastewater soft measurement model is affected by kernel function, this paper also focuses on the learning kernel function performance and parameters, then finds Multi-attributes Gaussian kernel which has good features and performance is suitable as kernel function of the sewage soft measurement model.Secondly, taking into account the advantages of Relevance Vector Machine and the analysis of kernel function, a soft measurement model based on Multi-attributes Gaussian kernel Relevance Vector Machine is proposed to predict sewage parameters BOD and COD, and the genetic algorithm is used to optimize the kernel parameter in the study of the Multi-attributes Gaussian kernel. Experiments show that the model can achieve better BOD prediction, but the COD prediction is still to be improved. Aiming at the problem that the genetic algorithm is difficult to obtain the suitable kernel parameters in COD prediction, the gradient descent method is used to study kernel parameter. Experiments show that the model has better accuracy than the model based on genetic algorithm in COD prediction, and it has low sensitivity and robustness.Then, in order to further improve the prediction accuracy of the important parameters of the wastewater, this paper proposes Multi-attributes Gaussian kernel Relevance Vector Machine model based on self-optimization. In view of the learning problem of kernel parameter, the self-optimization learning method is proposed, and its implementation steps are given. Experiments show that the model not only has low sensitivity and good robustness, but also can obtain high output accuracy while ensuring the sparse and convergence of the model and can better predict effluent parameters of sewage.Finally, offline model is difficult to guarantee the prediction effect of the subsequent operating point after a long time. It exhibits poor adaptability in some operating point. In order to solve this problem, this paper proposes an online soft measurement model based on Multi-attributes Gaussian kernel fast Relevance Vector Machine. The model uses the Relevance Vector Machine in the Bayesian framework to predict the output index, and introduces the fast marginal likelihood algorithm to speed up the update rate. Experiments show that the model not only can effectively track the change of BOD and COD, but also has faster update speed, and it can achieve the online real-time measurement of effluent quality of sewage treatment.