Research Of Gross Error Detection In Data Reconciliation And Support Vector Machine

Plant data are playing a more and more important role with the development of information technology. Plant data are used for not only a series of monitoring tasks but also other plant activities such as process control, process optimization, production scheduling, cost control and so on. However, these data usually contain random error and possibly gross error.These errors, especially gross error,cause great harm to the industrial production. In addition, support vector machine develops rapidlyin recent years in recent years. However, the existence of gross error hindered its widespread application.This dissertation focuses on gross error and research the method of dealing with gross error in the field of data recionciliation and support vector regression.1,A MT-NT-MILP (MNM)combined method is developed for gross error detection and data reconciliation for industrial application. In our new method,MT method and NT method are combined to generate gross error candidates by maximal spanning tree before data rectification. Then the candidates are used in the MILP model to improve the efficiency by reducing the number of binary variables. This combination is helpful to overcome the defect of MT,NT and MILP method.Simulation results show that our proposed method overcomes other methods and the method is effective especially in a large-scale problem.2,Mixed integer linear programming (MILP) approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance constrains for linear system. However, under the MILP framework, it is difficult for model expansion. In this paper, it proves that the MILP model for data reconciliation is equal to a nonlinear programming model, therefore it could be resolved not only by the mixed integer linear programming but also by iterative method. The results obtained by applying the two algorithms are the same. That also verifies the correctness of the conclusion. Under the nonlinear programming framework, an extended model is presented. Besides we successfully apply MILP method for dynamic system (CSTR).3,With the information technology applied widely to process industry, a large amount of historical data used for obtaining the prior probabilities of gross error occurrence is stored in database. To use the historical data to enhance the efficiency of gross error detection and data reconciliation, a new strategy which includes two steps is proposed. The first step is that mixed integer program technique is incorporated to use the prior information to find out gross errors. The second step is to eliminate all detected gross errors and adjust process data with material, energy, and other balance constrains. In this step an improved method is proposed to achieve the same effect with traditional method through adjusting the covariance matrix.The new criteria is designed to differentiate different prior information. Performance of this new strategy is compared and discussed by applying the strategy for a challenging test problem. Simulation results show the effectiveness of our method.4,A novel support vector machine for regression, which combines robust estimators (Fair estimator is chosen in this paper) and the smoothing technique, is proposed and called as smoothε-insensitive Fair estimator support vector machine for regression (ε-SFSVR). In theε-SFSVR,a new type ofε-insensitive loss function, called asε-insensitive Fair estimator,is proposed by combining Fair estimator. With this loss function robustness could be improved and sparseness property may be remained. To enhance the learning speed,we apply the smoothing techniques that have been used for solving the support vector machine for regression, to replace theε-insensitive Fair estimator by an accurate smooth approximation. This will allow us to solveε-SFSVR as an unconstrained minimization problem directly. We also prescribe a Newton-Armijo algorithm that has been shown to be convergent globally and quadratically to solve ourε-SFSVR. Based on the simulation results, the proposed approach has fast learning speed and better generalization performance whether outliers exist or not.5,One robust non-convex loss function is constructed by combining two differentiable convex functions because non-convex loss functions own advantage over convex ones in robustness and generalization performance for support vector regression. With this non-convex loss function, a flatheaded support vector regression (FSVR) is proposed and then the concave-convex procedure is used to solve the FSVR by transforming the non-convex problem into a sequence of convex ones. The FSVR integrates the advantage of standard SVR and weighted SVR, in other words, it can not only obtain better sparseness property but also restrain the outliers of training samples. Experiments have been done on artificial and benchmark datasets and the results show the effectiveness of the proposed FSVR.Finally,the dissertation is concluded with a summary and prospect of future researches.