This study describes a classification methodology based on support vector machines(SVMs),which offer superior classification performance for fault diagnosis in chemical process engineering.The method incorporates an efficient parameter tuning procedure(based on minimization of radius/margin bound for SVM's leave-one-out errors)into a multi-class classification strategy using a fuzzy decision factor,which is named fuzzy support vector machine(FSVM).The datasets generated from the Tennessee Eastman process(TEP)simulator were used to evaluate the clas-sification performance.To decrease the negative influence of the auto-correlated and irrelevant variables,a key vari-able identification procedure using recursive feature elimination,based on the SVM is implemented,with time lags incorporated,before every classifier is trained,and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation.Performance comparisons are implemented among several kinds of multi-class decision machines,by which the effectiveness of the proposed approach is proved.
In this study, we present a constructive algorithm for training cooperative supportvector machine ensembles (CSVMEs). CSVME combines ensemble architecturedesign with cooperative training for individual SVMs in ensembles. Unlike mostprevious studies on training ensembles, CSVME puts emphasis on both accuracyand collaboration among individual SVMs in an ensemble. A group of SVMs se-lected on the basis of recursive classifier elimination is used in CSVME, and thenumber of the individual SVMs selected to construct CSVME is determined by10-fold cross-validation. This kind of SVME has been tested on two ovarian can-cer datasets previously obtained by proteomic mass spectrometry. By combiningseveral individual SVMs, the proposed method achieves better performance thanthe SVME of all base SVMs.
Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.
