{"title":"智能的计算理论:反馈","authors":"Daniel Kovach","doi":"10.4236/IJMNTA.2017.62006","DOIUrl":null,"url":null,"abstract":"In this paper we discuss the applications of feedback to intelligent agents. We show that it adds a momentum component to the learning algorithm. We derive via Lyapunov stability theory the condition necessary in order that the entropy minimization principal of computational intelligence is preserved in the presence of feedback.","PeriodicalId":69680,"journal":{"name":"现代非线性理论与应用(英文)","volume":"06 1","pages":"70-73"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"The Computational Theory of Intelligence: Feedback\",\"authors\":\"Daniel Kovach\",\"doi\":\"10.4236/IJMNTA.2017.62006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we discuss the applications of feedback to intelligent agents. We show that it adds a momentum component to the learning algorithm. We derive via Lyapunov stability theory the condition necessary in order that the entropy minimization principal of computational intelligence is preserved in the presence of feedback.\",\"PeriodicalId\":69680,\"journal\":{\"name\":\"现代非线性理论与应用(英文)\",\"volume\":\"06 1\",\"pages\":\"70-73\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"现代非线性理论与应用(英文)\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.4236/IJMNTA.2017.62006\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"现代非线性理论与应用(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.4236/IJMNTA.2017.62006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Computational Theory of Intelligence: Feedback
In this paper we discuss the applications of feedback to intelligent agents. We show that it adds a momentum component to the learning algorithm. We derive via Lyapunov stability theory the condition necessary in order that the entropy minimization principal of computational intelligence is preserved in the presence of feedback.
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