{"title":"一种求解多级互联网络流量控制问题的神经网络算法","authors":"K. T. Sun, H. Fu","doi":"10.1109/IJCNN.1991.170549","DOIUrl":null,"url":null,"abstract":"The authors propose a neural network algorithm for the traffic control problem (an NP-complete problem) in multistage interconnection networks. The traffic control problem can be represented by an energy function, and the state of the energy function is iteratively updated by the authors' parallel algorithm. When the energy function reaches a stable state, the state represents a solution of the problem. Empirical results show the effectiveness of the proposed algorithm, and the time complexity with n/sup 2/ neurons is O(n log n). Simulation results show that both the throughput and iteration steps are much better than in the linear approach. Furthermore, since the traffic control problem can be reduced to the traveling salesman problem. the proposed algorithm can also be applied to other optimization problems.<<ETX>>","PeriodicalId":211135,"journal":{"name":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1991-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A neural network algorithm for solving the traffic control problem in multistage interconnection networks\",\"authors\":\"K. T. Sun, H. Fu\",\"doi\":\"10.1109/IJCNN.1991.170549\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors propose a neural network algorithm for the traffic control problem (an NP-complete problem) in multistage interconnection networks. The traffic control problem can be represented by an energy function, and the state of the energy function is iteratively updated by the authors' parallel algorithm. When the energy function reaches a stable state, the state represents a solution of the problem. Empirical results show the effectiveness of the proposed algorithm, and the time complexity with n/sup 2/ neurons is O(n log n). Simulation results show that both the throughput and iteration steps are much better than in the linear approach. Furthermore, since the traffic control problem can be reduced to the traveling salesman problem. the proposed algorithm can also be applied to other optimization problems.<<ETX>>\",\"PeriodicalId\":211135,\"journal\":{\"name\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.1991.170549\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1991.170549","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A neural network algorithm for solving the traffic control problem in multistage interconnection networks
The authors propose a neural network algorithm for the traffic control problem (an NP-complete problem) in multistage interconnection networks. The traffic control problem can be represented by an energy function, and the state of the energy function is iteratively updated by the authors' parallel algorithm. When the energy function reaches a stable state, the state represents a solution of the problem. Empirical results show the effectiveness of the proposed algorithm, and the time complexity with n/sup 2/ neurons is O(n log n). Simulation results show that both the throughput and iteration steps are much better than in the linear approach. Furthermore, since the traffic control problem can be reduced to the traveling salesman problem. the proposed algorithm can also be applied to other optimization problems.<>