{"title":"基于新扰动的BP神经网络控制混沌系统","authors":"Xiaoping Zong, Jun Geng","doi":"10.1109/ICWAPR.2009.5207408","DOIUrl":null,"url":null,"abstract":"A new perturbation model is proposed, and used to train BP neural network for chaotic systems in this paper. The method requires no previous knowledge about the system to be controlled, including the dimensionality of the system and location of unstable fixed points, can be extended to other chaos control. It was tested on the henon and Logistic maps, and the simulation results showed that it could make the chaos present periodic motion.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Control chaotic systems based on BP neural network with a new perturbation\",\"authors\":\"Xiaoping Zong, Jun Geng\",\"doi\":\"10.1109/ICWAPR.2009.5207408\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new perturbation model is proposed, and used to train BP neural network for chaotic systems in this paper. The method requires no previous knowledge about the system to be controlled, including the dimensionality of the system and location of unstable fixed points, can be extended to other chaos control. It was tested on the henon and Logistic maps, and the simulation results showed that it could make the chaos present periodic motion.\",\"PeriodicalId\":424264,\"journal\":{\"name\":\"2009 International Conference on Wavelet Analysis and Pattern Recognition\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Wavelet Analysis and Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICWAPR.2009.5207408\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Wavelet Analysis and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICWAPR.2009.5207408","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Control chaotic systems based on BP neural network with a new perturbation
A new perturbation model is proposed, and used to train BP neural network for chaotic systems in this paper. The method requires no previous knowledge about the system to be controlled, including the dimensionality of the system and location of unstable fixed points, can be extended to other chaos control. It was tested on the henon and Logistic maps, and the simulation results showed that it could make the chaos present periodic motion.
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