In recent years, advanced control technologies have been used for the optimum control of a cold storage. But there are still a lot of shortcomings. One of the main problems is that the traditional methods can't re...In recent years, advanced control technologies have been used for the optimum control of a cold storage. But there are still a lot of shortcomings. One of the main problems is that the traditional methods can't realize the on-line predictive optimum control of a refrigerating system with simple and valid algorithms. An RBF neural network has a strong ability in nonlinear mapping, a good interpolating value performance, and a higher training speed. Thus a two-stage RBF neural network is proposed in this paper. Combining the measured values with the predicted values, the two-stage RBF neural network is used for the on-line predictive optimum control of the cold storage temperature. The application results of the new methods show a great success.展开更多
Robust discrete quasi-sliding mode tracking controller is developed with discrete reaching law for trajectory tracking in the presence of modeling uncertainty and unknown disturbance. The conventional prior knowledge ...Robust discrete quasi-sliding mode tracking controller is developed with discrete reaching law for trajectory tracking in the presence of modeling uncertainty and unknown disturbance. The conventional prior knowledge of uncertainty upper bounds being replaced by an on-line estimation used in the controller to cancel the slowly varying uncertainties by the mechanism of time delay. This method reduces the feedback gain substantially and improves tracking accuracy. The system behavior in the vicinity of the sliding surface is examined for the existence and bandwidth of quasi-sliding mode. Simulation results show the effectiveness of the strategy.展开更多
The ratings in many user-object online rating systems can reflect whether users like or dislike the objects,and in some online rating systems,users can directly choose whether to like an object.So these systems can be...The ratings in many user-object online rating systems can reflect whether users like or dislike the objects,and in some online rating systems,users can directly choose whether to like an object.So these systems can be represented by signed bipartite networks,but the original unsigned node evaluation algorithm cannot be directly used on the signed networks.This paper proposes the Signed Page Rank algorithm for signed bipartite networks to evaluate the object and user nodes at the same time.Based on the global information,the nodes can be sorted by the Signed Page Rank values in descending order,and the result is SR Ranking.The authors analyze the characteristics of top and bottom nodes of the real networks and find out that for objects,the SR Ranking can provide a more reasonable ranking which combines the degree and rating of node,and the algorithm also can help us to identify users with specific rating patterns.By discussing the location of negative edges and the sensitivity of object SR Ranking to negative edges,the authors also explore that the negative edges play an important role in the algorithm and explain that why the bad reviews are more important in real networks.展开更多
文摘In recent years, advanced control technologies have been used for the optimum control of a cold storage. But there are still a lot of shortcomings. One of the main problems is that the traditional methods can't realize the on-line predictive optimum control of a refrigerating system with simple and valid algorithms. An RBF neural network has a strong ability in nonlinear mapping, a good interpolating value performance, and a higher training speed. Thus a two-stage RBF neural network is proposed in this paper. Combining the measured values with the predicted values, the two-stage RBF neural network is used for the on-line predictive optimum control of the cold storage temperature. The application results of the new methods show a great success.
文摘Robust discrete quasi-sliding mode tracking controller is developed with discrete reaching law for trajectory tracking in the presence of modeling uncertainty and unknown disturbance. The conventional prior knowledge of uncertainty upper bounds being replaced by an on-line estimation used in the controller to cancel the slowly varying uncertainties by the mechanism of time delay. This method reduces the feedback gain substantially and improves tracking accuracy. The system behavior in the vicinity of the sliding surface is examined for the existence and bandwidth of quasi-sliding mode. Simulation results show the effectiveness of the strategy.
基金supported by the National Natural Science Foundation of China under Grant Nos.61573065and 71731002。
文摘The ratings in many user-object online rating systems can reflect whether users like or dislike the objects,and in some online rating systems,users can directly choose whether to like an object.So these systems can be represented by signed bipartite networks,but the original unsigned node evaluation algorithm cannot be directly used on the signed networks.This paper proposes the Signed Page Rank algorithm for signed bipartite networks to evaluate the object and user nodes at the same time.Based on the global information,the nodes can be sorted by the Signed Page Rank values in descending order,and the result is SR Ranking.The authors analyze the characteristics of top and bottom nodes of the real networks and find out that for objects,the SR Ranking can provide a more reasonable ranking which combines the degree and rating of node,and the algorithm also can help us to identify users with specific rating patterns.By discussing the location of negative edges and the sensitivity of object SR Ranking to negative edges,the authors also explore that the negative edges play an important role in the algorithm and explain that why the bad reviews are more important in real networks.
