{"title":"SIRM连通模糊推理模型及其在一阶滞后系统和二阶滞后系统中的应用","authors":"N. Yubazaki, J. Yi, M. Otani, K. Hirota","doi":"10.1109/AFSS.1996.583707","DOIUrl":null,"url":null,"abstract":"SIRMs (Single Input Rule Modules) Connected Fuzzy inference Model is proposed for multiple input fuzzy control. In the model, the importance degree is defined first and single input fuzzy rule module is constructed for each input item. The model output is obtained by summarizing the production of the importance degree and the fuzzy inference result of each module. The proposed model needs both very few rules and parameters and the rules can be designed much easier. Moreover, the role of each input item can be strengthened or weakened by changing its importance degree according to experts' intuitive experiences. The proposed model is applied to typical first order lag systems and second order lag systems to confirm the improvement in control performance compared with the conventional model.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"SIRM's connected fuzzy inference model and its applications to first-order lag systems and second-order lag systems\",\"authors\":\"N. Yubazaki, J. Yi, M. Otani, K. Hirota\",\"doi\":\"10.1109/AFSS.1996.583707\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"SIRMs (Single Input Rule Modules) Connected Fuzzy inference Model is proposed for multiple input fuzzy control. In the model, the importance degree is defined first and single input fuzzy rule module is constructed for each input item. The model output is obtained by summarizing the production of the importance degree and the fuzzy inference result of each module. The proposed model needs both very few rules and parameters and the rules can be designed much easier. Moreover, the role of each input item can be strengthened or weakened by changing its importance degree according to experts' intuitive experiences. The proposed model is applied to typical first order lag systems and second order lag systems to confirm the improvement in control performance compared with the conventional model.\",\"PeriodicalId\":197019,\"journal\":{\"name\":\"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AFSS.1996.583707\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AFSS.1996.583707","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SIRM's connected fuzzy inference model and its applications to first-order lag systems and second-order lag systems
SIRMs (Single Input Rule Modules) Connected Fuzzy inference Model is proposed for multiple input fuzzy control. In the model, the importance degree is defined first and single input fuzzy rule module is constructed for each input item. The model output is obtained by summarizing the production of the importance degree and the fuzzy inference result of each module. The proposed model needs both very few rules and parameters and the rules can be designed much easier. Moreover, the role of each input item can be strengthened or weakened by changing its importance degree according to experts' intuitive experiences. The proposed model is applied to typical first order lag systems and second order lag systems to confirm the improvement in control performance compared with the conventional model.