Design and simulation of support vector machines generalized observer

Support vector machines generalized observer (SGO) was proposed and its design was introduced. The observer employs support vector machines (SVM) regression algorithm for fitting the nonlinearity among process variables. It was derived by process inputs and outputs except for the output to be monitored and can be used for process fault detection and fault tolerance control, multi-variables chemical process simulation results show that SVM overcomes some disadvantages of artificial neural network (ANN), such as over fitting, local minimization and difficulties in structure selection. SGO has a quite precise output in application.