SVM (support vector machines) have become an increasingly popular tool for machine learning tasks involving classification,regression or novelty detection.In particular,they exhibit good generalization performance on ...SVM (support vector machines) have become an increasingly popular tool for machine learning tasks involving classification,regression or novelty detection.In particular,they exhibit good generalization performance on many real issues and the approach is properly motivated theoretically.There are relatively a few free parameters to adjust and the architecture of the learning machine does not need to be found by experimentation.In this paper,survey of the key contents on this subject,focusing on the most well-known models based on kernel substitution,namely SVM,as well as the activated fields at present and the development tendency,is presented.展开更多
The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996), and has the following advantages: first, Gree...The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996), and has the following advantages: first, Greedy algorithm substitutes for R*-tree (Bechmann et al., 1990) in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second, the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally, authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.展开更多
The salt-gradient operation mode used in ion-exchange simulated moving bed chromatography (SMBC) can improve the efficiency of protein separations. A detailed model that takes into account any kind of adsorption/ion-e...The salt-gradient operation mode used in ion-exchange simulated moving bed chromatography (SMBC) can improve the efficiency of protein separations. A detailed model that takes into account any kind of adsorption/ion-exchange equilibrium, salt gradient, size exclusion, mass transfer resistance, and port periodic switching mechanism, was developed to simulate the complex dynamics. The model predictions were verified by the experimental data on upward and downward gradients for protein separations reported in the literature. All design and operating parameters (number, configuration, length and diameter of columns, particle size, switching period, flow rates of feed, raffinate, desorbent and extract, protein concentrations in feed, different salt concentrations in desorbent and feed) can be chosen correctly by numerical simulation. This model can facilitate the design, operation, optimization, control and scale-up of salt-gradient ion-exchange SMBC for protein separations.展开更多
The fuzzy NN predictive control algorithm introduced in this paper uses fuzzy neural network to model the nonlinear MIMO process. Its training method that integrates LS and BP algorithm brings quick convergence. GPC a...The fuzzy NN predictive control algorithm introduced in this paper uses fuzzy neural network to model the nonlinear MIMO process. Its training method that integrates LS and BP algorithm brings quick convergence. GPC algorithm is used as the predictive component. The fuzzy neural network has six layers, including input layer, output layer and four hidden layers. An application to a MIMO nonlinear process(green liquor system of the recovery system in a pulp factory shows that this algorithm has better performance than normal PID algrithm.展开更多
Given that the overlapping of jobs is permitted, the paper studies the scheduling and control of failure prone production systems, i.e. so-called settings with demand uncertainty and job overlaps. Because a variable d...Given that the overlapping of jobs is permitted, the paper studies the scheduling and control of failure prone production systems, i.e. so-called settings with demand uncertainty and job overlaps. Because a variable demand resource is involved in the production and corrective maintenance control problems of the system, which switched randomly between zero and a maximum level, it is difficult to obtain the analytical solutions of the optimal single hedging point policy. An asymptotic optimal scheduling policy is presented and a double hedging point policy is offered to control simultaneously the production rate and the corrective maintenance rate of the system. The corresponding analytical solutions and approximate solutions are obtained. Considering the relationship of production, corrective maintenance and demand variable, an approximate optimal single hedging point control policy is proposed. Numerical results are presented.展开更多
This paper presents a two-stage robust model predictive control (RMPC) algorithm named as IRMPC for uncertain linear integrating plants described by a state-space model with input constraints. The global convergence o...This paper presents a two-stage robust model predictive control (RMPC) algorithm named as IRMPC for uncertain linear integrating plants described by a state-space model with input constraints. The global convergence of the resulted closed loop system is guaranteed under mild assumption. The simulation example shows its validity and better performance than conventional Min-Max RMPC strategies.展开更多
基金Supported by the National863Plan Foundation of China( 2 0 0 2 AA41 2 0 1 0 )
文摘SVM (support vector machines) have become an increasingly popular tool for machine learning tasks involving classification,regression or novelty detection.In particular,they exhibit good generalization performance on many real issues and the approach is properly motivated theoretically.There are relatively a few free parameters to adjust and the architecture of the learning machine does not need to be found by experimentation.In this paper,survey of the key contents on this subject,focusing on the most well-known models based on kernel substitution,namely SVM,as well as the activated fields at present and the development tendency,is presented.
文摘The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996), and has the following advantages: first, Greedy algorithm substitutes for R*-tree (Bechmann et al., 1990) in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second, the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally, authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.
文摘The salt-gradient operation mode used in ion-exchange simulated moving bed chromatography (SMBC) can improve the efficiency of protein separations. A detailed model that takes into account any kind of adsorption/ion-exchange equilibrium, salt gradient, size exclusion, mass transfer resistance, and port periodic switching mechanism, was developed to simulate the complex dynamics. The model predictions were verified by the experimental data on upward and downward gradients for protein separations reported in the literature. All design and operating parameters (number, configuration, length and diameter of columns, particle size, switching period, flow rates of feed, raffinate, desorbent and extract, protein concentrations in feed, different salt concentrations in desorbent and feed) can be chosen correctly by numerical simulation. This model can facilitate the design, operation, optimization, control and scale-up of salt-gradient ion-exchange SMBC for protein separations.
文摘The fuzzy NN predictive control algorithm introduced in this paper uses fuzzy neural network to model the nonlinear MIMO process. Its training method that integrates LS and BP algorithm brings quick convergence. GPC algorithm is used as the predictive component. The fuzzy neural network has six layers, including input layer, output layer and four hidden layers. An application to a MIMO nonlinear process(green liquor system of the recovery system in a pulp factory shows that this algorithm has better performance than normal PID algrithm.
基金This work was supported by the Project 973 (No.2002CB312200) and the National Natural Science Foundation (No.60404018).
文摘Given that the overlapping of jobs is permitted, the paper studies the scheduling and control of failure prone production systems, i.e. so-called settings with demand uncertainty and job overlaps. Because a variable demand resource is involved in the production and corrective maintenance control problems of the system, which switched randomly between zero and a maximum level, it is difficult to obtain the analytical solutions of the optimal single hedging point policy. An asymptotic optimal scheduling policy is presented and a double hedging point policy is offered to control simultaneously the production rate and the corrective maintenance rate of the system. The corresponding analytical solutions and approximate solutions are obtained. Considering the relationship of production, corrective maintenance and demand variable, an approximate optimal single hedging point control policy is proposed. Numerical results are presented.
文摘This paper presents a two-stage robust model predictive control (RMPC) algorithm named as IRMPC for uncertain linear integrating plants described by a state-space model with input constraints. The global convergence of the resulted closed loop system is guaranteed under mild assumption. The simulation example shows its validity and better performance than conventional Min-Max RMPC strategies.
