{"title":"基于混沌神经网络的改进QoS组播路由算法","authors":"Ming Sun, Qingliang Zhang, Wei Cao","doi":"10.1109/IHMSC.2015.240","DOIUrl":null,"url":null,"abstract":"In the process of using Chaotic Neural Network to resolve QoS (Quality of Service) multicast routing, this paper adds new constraint items on the basis of the original energy function and presents a new energy function, which solves the problem that the traditional algorithm is easy to fall into local minima and invalid solutions for not strictly restricting \"row\" and \"column\" items in the energy function. Simulation results show that, compared with the original energy function, the newly constructed energy function can avoid the chaotic neural network converging to the invalid solution and local optimal solution in a certain extent, and can improve the convergent speed of the network.","PeriodicalId":6592,"journal":{"name":"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics","volume":"8 1","pages":"139-143"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An Improved QoS Multicast Routing Algorithm Based on Chaotic Neural Network\",\"authors\":\"Ming Sun, Qingliang Zhang, Wei Cao\",\"doi\":\"10.1109/IHMSC.2015.240\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the process of using Chaotic Neural Network to resolve QoS (Quality of Service) multicast routing, this paper adds new constraint items on the basis of the original energy function and presents a new energy function, which solves the problem that the traditional algorithm is easy to fall into local minima and invalid solutions for not strictly restricting \\\"row\\\" and \\\"column\\\" items in the energy function. Simulation results show that, compared with the original energy function, the newly constructed energy function can avoid the chaotic neural network converging to the invalid solution and local optimal solution in a certain extent, and can improve the convergent speed of the network.\",\"PeriodicalId\":6592,\"journal\":{\"name\":\"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"volume\":\"8 1\",\"pages\":\"139-143\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IHMSC.2015.240\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IHMSC.2015.240","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
在利用混沌神经网络求解QoS (Quality of Service)组播路由的过程中,本文在原有能量函数的基础上增加了新的约束项,提出了一种新的能量函数,解决了传统算法由于对能量函数中的“行”和“列”项没有严格限制而容易陷入局部极小和无效解的问题。仿真结果表明,与原能量函数相比,新构造的能量函数在一定程度上避免了混沌神经网络收敛到无效解和局部最优解,提高了网络的收敛速度。
An Improved QoS Multicast Routing Algorithm Based on Chaotic Neural Network
In the process of using Chaotic Neural Network to resolve QoS (Quality of Service) multicast routing, this paper adds new constraint items on the basis of the original energy function and presents a new energy function, which solves the problem that the traditional algorithm is easy to fall into local minima and invalid solutions for not strictly restricting "row" and "column" items in the energy function. Simulation results show that, compared with the original energy function, the newly constructed energy function can avoid the chaotic neural network converging to the invalid solution and local optimal solution in a certain extent, and can improve the convergent speed of the network.