{"title":"随机非线性动力学模式形成和增长模型。","authors":"Leonid P Yaroslavsky","doi":"10.1186/1753-4631-1-4","DOIUrl":null,"url":null,"abstract":"<p><p> Stochastic evolutionary growth and pattern formation models are treated in a unified way in terms of algorithmic models of nonlinear dynamic systems with feedback built of a standard set of signal processing units. A number of concrete models is described and illustrated by numerous examples of artificially generated patterns that closely imitate wide variety of patterns found in the nature.</p>","PeriodicalId":87480,"journal":{"name":"Nonlinear biomedical physics","volume":"1 1","pages":"4"},"PeriodicalIF":0.0000,"publicationDate":"2007-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/1753-4631-1-4","citationCount":"6","resultStr":"{\"title\":\"Stochastic nonlinear dynamics pattern formation and growth models.\",\"authors\":\"Leonid P Yaroslavsky\",\"doi\":\"10.1186/1753-4631-1-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p> Stochastic evolutionary growth and pattern formation models are treated in a unified way in terms of algorithmic models of nonlinear dynamic systems with feedback built of a standard set of signal processing units. A number of concrete models is described and illustrated by numerous examples of artificially generated patterns that closely imitate wide variety of patterns found in the nature.</p>\",\"PeriodicalId\":87480,\"journal\":{\"name\":\"Nonlinear biomedical physics\",\"volume\":\"1 1\",\"pages\":\"4\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1186/1753-4631-1-4\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nonlinear biomedical physics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/1753-4631-1-4\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nonlinear biomedical physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/1753-4631-1-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stochastic nonlinear dynamics pattern formation and growth models.
Stochastic evolutionary growth and pattern formation models are treated in a unified way in terms of algorithmic models of nonlinear dynamic systems with feedback built of a standard set of signal processing units. A number of concrete models is described and illustrated by numerous examples of artificially generated patterns that closely imitate wide variety of patterns found in the nature.
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