Jie Cheng, Mohammad R Sayeh, Mehdi R Zargham, Qiang Cheng
{"title":"基于常微分方程的实时矢量量化和聚类。","authors":"Jie Cheng, Mohammad R Sayeh, Mehdi R Zargham, Qiang Cheng","doi":"10.1109/TNN.2011.2172627","DOIUrl":null,"url":null,"abstract":"<p><p>This brief presents a dynamical system approach to vector quantization or clustering based on ordinary differential equations with the potential for real-time implementation. Two examples of different pattern clusters demonstrate that the model can successfully quantize different types of input patterns. Furthermore, we analyze and study the stability of our dynamical system. By discovering the equilibrium points for certain input patterns and analyzing their stability, we have shown the quantizing behavior of the system with respect to its vigilance parameter. The proposed system is applied to two real-world problems, providing comparable results to the best reported findings. This validates the effectiveness of our proposed approach.</p>","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":" ","pages":"2143-8"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2172627","citationCount":"5","resultStr":"{\"title\":\"Real-time vector quantization and clustering based on ordinary differential equations.\",\"authors\":\"Jie Cheng, Mohammad R Sayeh, Mehdi R Zargham, Qiang Cheng\",\"doi\":\"10.1109/TNN.2011.2172627\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This brief presents a dynamical system approach to vector quantization or clustering based on ordinary differential equations with the potential for real-time implementation. Two examples of different pattern clusters demonstrate that the model can successfully quantize different types of input patterns. Furthermore, we analyze and study the stability of our dynamical system. By discovering the equilibrium points for certain input patterns and analyzing their stability, we have shown the quantizing behavior of the system with respect to its vigilance parameter. The proposed system is applied to two real-world problems, providing comparable results to the best reported findings. This validates the effectiveness of our proposed approach.</p>\",\"PeriodicalId\":13434,\"journal\":{\"name\":\"IEEE transactions on neural networks\",\"volume\":\" \",\"pages\":\"2143-8\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/TNN.2011.2172627\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on neural networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TNN.2011.2172627\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2011/10/31 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on neural networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TNN.2011.2172627","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2011/10/31 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Real-time vector quantization and clustering based on ordinary differential equations.
This brief presents a dynamical system approach to vector quantization or clustering based on ordinary differential equations with the potential for real-time implementation. Two examples of different pattern clusters demonstrate that the model can successfully quantize different types of input patterns. Furthermore, we analyze and study the stability of our dynamical system. By discovering the equilibrium points for certain input patterns and analyzing their stability, we have shown the quantizing behavior of the system with respect to its vigilance parameter. The proposed system is applied to two real-world problems, providing comparable results to the best reported findings. This validates the effectiveness of our proposed approach.