{"title":"计算智能在信号处理中的应用:模糊神经识别的建议","authors":"C. Bottura, G. L. de Oliveira Serra","doi":"10.1109/MLSP.2004.1422965","DOIUrl":null,"url":null,"abstract":"In this study an approach to fuzzy neural identification of MIMO discrete-time nonlinear dynamical systems is proposed. Based on the Takagi-Sugeno (TS) fuzzy neural network, off-line and on-line schemes are formulated as a NARX (nonlinear autoregressive with exogenous input) fuzzy neural model from samples of a nonlinear dynamical system where the consequent parameters are modified by an adaptive WIV (weighted instrumental variable) algorithm based on the numerically robust orthogonal householder transformation","PeriodicalId":70952,"journal":{"name":"信号处理","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2004-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computational intelligence applied to signal processing: a proposal for fuzzy neural identification\",\"authors\":\"C. Bottura, G. L. de Oliveira Serra\",\"doi\":\"10.1109/MLSP.2004.1422965\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study an approach to fuzzy neural identification of MIMO discrete-time nonlinear dynamical systems is proposed. Based on the Takagi-Sugeno (TS) fuzzy neural network, off-line and on-line schemes are formulated as a NARX (nonlinear autoregressive with exogenous input) fuzzy neural model from samples of a nonlinear dynamical system where the consequent parameters are modified by an adaptive WIV (weighted instrumental variable) algorithm based on the numerically robust orthogonal householder transformation\",\"PeriodicalId\":70952,\"journal\":{\"name\":\"信号处理\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-09-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"信号处理\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1109/MLSP.2004.1422965\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"信号处理","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/MLSP.2004.1422965","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computational intelligence applied to signal processing: a proposal for fuzzy neural identification
In this study an approach to fuzzy neural identification of MIMO discrete-time nonlinear dynamical systems is proposed. Based on the Takagi-Sugeno (TS) fuzzy neural network, off-line and on-line schemes are formulated as a NARX (nonlinear autoregressive with exogenous input) fuzzy neural model from samples of a nonlinear dynamical system where the consequent parameters are modified by an adaptive WIV (weighted instrumental variable) algorithm based on the numerically robust orthogonal householder transformation
期刊介绍:
Journal of Signal Processing is an academic journal supervised by China Association for Science and Technology and sponsored by China Institute of Electronics. The journal is an academic journal that reflects the latest research results and technological progress in the field of signal processing and related disciplines. It covers academic papers and review articles on new theories, new ideas, and new technologies in the field of signal processing. The journal aims to provide a platform for academic exchanges for scientific researchers and engineering and technical personnel engaged in basic research and applied research in signal processing, thereby promoting the development of information science and technology. At present, the journal has been included in the three major domestic core journal databases "China Science Citation Database (CSCD), China Science and Technology Core Journals (CSTPCD), Chinese Core Journals Overview" and Coaj. It is also included in many foreign databases such as Scopus, CSA, EBSCO host, INSPEC, JST, etc.