This paper addresses the problems of parameter estimation of multivariable stationary stochastic systems on the basis of observed output *** main contribution is to employ the expectation-maximisation(EM)method as a m...
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This paper addresses the problems of parameter estimation of multivariable stationary stochastic systems on the basis of observed output *** main contribution is to employ the expectation-maximisation(EM)method as a means for computation of the maximum-likelihood(ML)parameter estimation of the *** form of the expectation of the studied system subjected to Gaussian distribution noise is derived and parameter choice that maximizes the expectation is also *** results in an iterative algorithm for parameter estimation and the robust algorithm implementation based on technique of QR-factorization and Cholesky factorization is also ***,algorithmic properties such as non-decreasing likelihood value,necessary and sufficient conditions for the algorithm to arrive at a local stationary parameter,the convergence rate and the factors affecting the convergence rate are *** study shows that the proposed algorithm has attractive properties such as numerical stability,and avoidance of difficult initial conditions.
A new image compression approach is proposed based on weighted support vector regression (SVR) and image segmentation in this paper. An efficient segmentation technique is used to measure the smoothness of segmented b...
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A new image compression approach is proposed based on weighted support vector regression (SVR) and image segmentation in this paper. An efficient segmentation technique is used to measure the smoothness of segmented blocks and then the segmented blocks can be classified. On each classified subblock, the learning of support vector machine generates different weights adaptively to train different DCT-transformed image representations. This leads to lower quality degradation at higher compression ratios. Simulation results show the superiority of this approach over the reported SVR-based compression strategies in prediction ability and rate-distortion performance.
Based on P systems, this paper proposes a new multi-objective optimization algorithm (PMOA). Similar to P systems, PMOA has a cell-like structure. The structure is dynamic and its membranes merge and divide at differe...
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Based on P systems, this paper proposes a new multi-objective optimization algorithm (PMOA). Similar to P systems, PMOA has a cell-like structure. The structure is dynamic and its membranes merge and divide at different stages. The key rule of a membrane is the communication rule which is derived from P systems. Mutation rules are important for the algorithm, which has different ranges of mutation in different membranes. The cooperation of the two rules contributes to the diversity of the population, the conquest of the muhimodality of objective function and the convergence of algorithm. Moreover, the unique structure divides the whole population into several sub populations, which decreases the computational complexity. Almost a dozen popular algorithms are compared using several test problems. Simulation results illustrate that the PMOA has the best performance. Its solutions are closer to the true Pareto-optimal front
Discrete‐event chemical processes can be almost characterized as Discrete Event Dynamic Systems (DEDS). In this paper, we present a timed‐event‐multigraph (TEMG) and dioid based modeling method to describe synchron...
Discrete‐event chemical processes can be almost characterized as Discrete Event Dynamic Systems (DEDS). In this paper, we present a timed‐event‐multigraph (TEMG) and dioid based modeling method to describe synchronization, sharing and event quantity. On this algebraic basis, the optimal behaviors are found as extreme solutions of a system of inequations over a complete lattice.
Using Hybrid Petri Nets (HPNs) together with a continuous mathematical model, a combined modeling method for continuous‐process dynamic scheduling and supervisory control is presented. This method can describe both c...
Using Hybrid Petri Nets (HPNs) together with a continuous mathematical model, a combined modeling method for continuous‐process dynamic scheduling and supervisory control is presented. This method can describe both continuous characteristics of a continuous process and its discrete control strategies on the same level. An ideal binary mixture distillation column is used as an example to illustrate the proposed method. Based on the combined model, a supervisory control scheme is also provided.
By extending the system's state variables,a novel predictive functional controller has been *** structure of this controller is similar to that of classical proportional integral(PI)optimal controller and in-clude...
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By extending the system's state variables,a novel predictive functional controller has been *** structure of this controller is similar to that of classical proportional integral(PI)optimal controller and in-cludes a control block that can perform a feed-forward control of future P-step set *** considers both the state variables and the output errors in its cost function,which results in enhanced control performance compared with traditional state space predictive functional control(TSSPFC)methods that consider only the predictive output *** predictive functional controller(PFC)has been compared with TSSPFC in terms of tracking ability,dis-turbance rejection,and also based on its application to heavy oil coking *** results obtained show the effectiveness of the controller.
A splicing system based genetic algorithm is proposed to optimize dynamical radial basis function(RBF)neural network,which is used to extract valuable process information from input output *** novel RBF net-work train...
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A splicing system based genetic algorithm is proposed to optimize dynamical radial basis function(RBF)neural network,which is used to extract valuable process information from input output *** novel RBF net-work training technique includes the network structure into the set of function centers by compromising between the conflicting requirements of reducing prediction error and simultaneously decreasing model *** ef-fectiveness of the proposed method is illustrated through the development of dynamic models as a benchmark discrete example and a continuous stirred tank reactor by comparing with several different RBF network training methods.
In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reform-ing process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped ki...
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In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reform-ing process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped kinetics re-action network and has been proved to be quite effective in terms of industrial application. The primary objectives in-clude maximization of yield of the aromatics and minimization of the yield of heavy aromatics. Four reactor inlet tem-peratures, reaction pressure, and hydrogen-to-oil molar ratio are selected as the decision variables. A genetic algorithm, which is proposed by the authors and named as the neighborhood and archived genetic algorithm (NAGA), is applied to solve this multiobjective optimization problem. The relations between each decision variable and the two objectives are also proposed and used for choosing a suitable solution from the obtained Pareto set.
The binaphthol enantiomers separation process using simulation moving bed technology is simulated with the true moving bed approach （TMB）. In order to systematically optimize the process with multiple productive obj...
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The binaphthol enantiomers separation process using simulation moving bed technology is simulated with the true moving bed approach （TMB）. In order to systematically optimize the process with multiple productive objectives, this article develops a variant of tissue P system （TPS）. Inspired by general tissue P systems, the special TPS has a tissue-like structure with several membranes. The key rules of each membrane are the communication rule and mutation rule. These characteristics contribute to the diversity of the population, the conquest of the multimodal of objective function, and the convergence of algorithm. The results of comparison with a popular algorithm——the non-dominated sorting genetic algorithm 2（NSGA-2） illustrate that the new algorithm has satisfactory performance. Using the algorithm, this study maximizes synchronously several conflicting objectives, purities of different products, and productivity.
In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-s...
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In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-sensor technique becomes an indispensable method to implement real-time quality control. In this article, a new model of multi-inputs multi-outputs （MIMO） soft-sensor, which is constructed based on hybrid modeling technique, is proposed for these interactional variables. Data-driven modeling method and simplified first principle modelingmethod are combined in this model. Data-driven modeling method based on limited memory partial least squares（LM-PLS） *** is used to build soft-senor models for some secondary ***, the simplified first principle model is used to compute three primary variables on line. The proposed model has been used in practicalprocess; the results indicate that the proposed model is precise and efficient, and it is possible to realize on line quality control for compound fertilizer process.
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