{"title":"神经形态自适应控制","authors":"I. Bar-Kana, A. Guez","doi":"10.1109/CDC.1989.70449","DOIUrl":null,"url":null,"abstract":"A neuromorphic computing architecture for adaptive control of a class of nonlinear systems is presented. Starting with some prior assumptions about stabilizability of the plants, a stable unsupervised architecture is obtained. It is a parallel distributed architecture, and it is shown that it provides bounded tracking and asymptotic regulation, following a class of teacher models. The feasibility of the method is demonstrated for a robotic manipulator.<<ETX>>","PeriodicalId":156565,"journal":{"name":"Proceedings of the 28th IEEE Conference on Decision and Control,","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1989-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Neuromorphic adaptive control\",\"authors\":\"I. Bar-Kana, A. Guez\",\"doi\":\"10.1109/CDC.1989.70449\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A neuromorphic computing architecture for adaptive control of a class of nonlinear systems is presented. Starting with some prior assumptions about stabilizability of the plants, a stable unsupervised architecture is obtained. It is a parallel distributed architecture, and it is shown that it provides bounded tracking and asymptotic regulation, following a class of teacher models. The feasibility of the method is demonstrated for a robotic manipulator.<<ETX>>\",\"PeriodicalId\":156565,\"journal\":{\"name\":\"Proceedings of the 28th IEEE Conference on Decision and Control,\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1989-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 28th IEEE Conference on Decision and Control,\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.1989.70449\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 28th IEEE Conference on Decision and Control,","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.1989.70449","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A neuromorphic computing architecture for adaptive control of a class of nonlinear systems is presented. Starting with some prior assumptions about stabilizability of the plants, a stable unsupervised architecture is obtained. It is a parallel distributed architecture, and it is shown that it provides bounded tracking and asymptotic regulation, following a class of teacher models. The feasibility of the method is demonstrated for a robotic manipulator.<>
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