Research On Method And Application Of Soft Measurement Based On RS-SVM

Soft Sensor Technique is an effective approach which is used to solve some actual puzzles, caused by the unavailability of measurers in the controlling process variables measuring or the high price of measurers, in industrial process control systems.Support Vector Machine (SVM) is a novel powerful machine learning method based on statistical learning theory which observes the principle of structural risk minimization (ERM). Furthermore, the SVM algorithm is a quadratic programming problem that promises the extremal solution is the global optimum and thus makes it possible to have good learning and generalization performance under the situation of small-samples. SVM provides a framework to solve the learning problems characterized by small samples, nonlinearity, high dimension and local minima, which are difficult to be dealt with by other learning methods.Rough set theory is a theoretical method which is applied to study the expression, learning and inducing of incomplete and uncertain data. It can reduce the knowledge- expression space, cancel the redundant information via describing the importance of different attribute in knowledge expression without prior knowledge.Since data acquired in practical production are always limited and a great number of sensors are demanded in a complicated process control system, the number of sensors has a direct influence on the investment. The key point of this thesis is how to decrease some sensors testing unimportant variables and insures a high predictive classification accuracy on the bases of limited small-samples.This thesis proposes a soft-sensor modeling method based on RS-SVM after analyzing some common soft-sensor modeling methods and considering the advantage of SVM and RS. refining the number of properties by using the attribute reduction theory of RS and taking advantage of the good generalization performance of SVM to modeling for the Soft-sensor problem of small examples, nonlinear and high dimensions.This dissertation is along the thinking of " Soft Sensor Technique Rough Set Theory Support Vector Machine Theory the combination method of RS and SVM the application of RS-SVM method in Soft-Sensor " to introduce the achievement concluded in the subject study. The most essential part is the last two parts, namely, the combination method of RS and SVM, as well as the application of RS-SVM method in...