Regression forecast of main steam flow based on mean impact value and support vector regression

Abstract

A new prediction model of main steam flow was come up with based on mean impact value (MIV) and support vector regression (SVR). At present, the main steam flow of large capacity unit is calculated by the Fulugel formula using the pressure after regulating stage and other parameters. Because of changing of flow path condition, varying load and other reasons, the working condition may not satisfied the Fulugel formula, and the result calculated by the Fulugel formula often has large error. Nowadays, researchers tend to take advantage of soft measurement. In this paper, mean impact value method in mathematics is introduced to the main steam flow prediction model of support vector regression, using mean impact value method to choose input variables that meet the requirements of model. The results show that, the mean impact value can effectively select the variables, and get 9 important variables from 16 initial variables, which greatly reduce the dimensions of model. In addition, the new model that combines the mean impact value with support vector machine can obtain much satisfied prediction precision than the model without getting rid of the not important variables. The maximal relative error is 3.1281 percent and the average absolute relative error is 0.9833 percent in the model without selecting variables. Compared with the former model, maximal relative error of the main steam flow predicted by the new model is 1.2248 percent, and the average absolute relative error is only 0.3687 percent. Obviously, the new model is capable of better generalization ability and high precision. Accurate measurement of main steam flow is significant to improve the online economic analysis reliability of units.

Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography

“…This way, the model was simplified, the model accuracy was improved, and the model was more favorable for the continuous monitoring of blood pressure. In this study, the variable screening was performed using the BP neural network with the mean impact value (MIV) [38,39] method, finding out the characteristic parameters with big impacts on the model output. The detailed process was as follows:…”