Based On Support Vector Machines Research In Temperature Control System

It's a broad application to measure and control temperature in the industry production. Especially in some industry, such as oil, chemistry, electric power, metallurgy, it's a very important cache to measure and control temperature, temperature parameter is all important index in industry control situation.The control of product quality is the core of all kinds of control, so the information of or about product quality of important process variables must be got in time to have good control of product quality. In the most real production processes, an experienced human operator may have little knowledge about a complex system, but can still regulate control systems satisfactorily by observing the signals of inputs and outputs. Therefore, this behavior of mimicking the human ability by machine learning is an effective technique means.Studying the statistical classification or regression problem based on a given finite amount of samples, the researchers proposed the statistical learning theory (SLT). As a new technique for machine learning, the statistical learning theory is gaining more popularity due to distinguished properties and promising application performance. Support vector machine(SVM), one of novel machine learning methods, is to find a fine balance between the training error and the complexity of the learning machine. Because the formulation of SVM is guided by the statistical learning theory (i.e., structure risk minimization principle, Vapnik-Chervonenkis theory), these properties ensure that SVM can obtain global solutions instead of trapping in local optimal solutions under finite samples.Especially, LS-SVM has a significant advantage of the lower computational complexity than the other support vector machine formulations using linear or nonlinear mathematical programming. Therefore, Least squares support vector machine has shown an excellent classification or regression performance in many applications.After identifying system and building its predictive model by LS-SVM, we propose temperature predictive control. According to the experimental result, predictive model by LS-SVM indicated effect achieves anticipation in this temperature system.