{"title":"非线性无线电工程系统的逻辑-功能建模","authors":"V. Kravchenko, D. Shirapov","doi":"10.1109/FAREASTCON.2018.8602769","DOIUrl":null,"url":null,"abstract":"This article is devoted to computer modeling of radio systems. The paper shows a method for automated solution of direct and inverse problems of mathematical modeling of nonlinear radio engineering systems. Nonlinear radio engineering systems perform important signal generation and conversion operations: amplification, modulation, detection, frequency multiplication and conversion. Modeling of such systems is necessary to improve the efficiency of their design and research. Existing modeling systems are application packages, that are limited to a set of valid tasks. They usually solve only direct problems (problems of system analysis). Modern radio engineering requires the ability to solve various direct and inverse problems of systems modeling within a uniform program complex. Therefore, the application packages should be replaced by complexes of automated modeling using elements of artificial intelligence. The article proposes a method of using functional grammars to construct the system of automated modeling of nonlinear semiconductor systems. The result of the scientific research is the logical-mathematical model using the terms of functional context-free grammars. The model substantiated by a logical conclusion using lambda calculus. Practical application of the method is demonstrated by examples of solving direct and inverse modeling problems.","PeriodicalId":177690,"journal":{"name":"2018 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Logic-Functional Modeling of Nonlinear Radio Engineering Systems\",\"authors\":\"V. Kravchenko, D. Shirapov\",\"doi\":\"10.1109/FAREASTCON.2018.8602769\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article is devoted to computer modeling of radio systems. The paper shows a method for automated solution of direct and inverse problems of mathematical modeling of nonlinear radio engineering systems. Nonlinear radio engineering systems perform important signal generation and conversion operations: amplification, modulation, detection, frequency multiplication and conversion. Modeling of such systems is necessary to improve the efficiency of their design and research. Existing modeling systems are application packages, that are limited to a set of valid tasks. They usually solve only direct problems (problems of system analysis). Modern radio engineering requires the ability to solve various direct and inverse problems of systems modeling within a uniform program complex. Therefore, the application packages should be replaced by complexes of automated modeling using elements of artificial intelligence. The article proposes a method of using functional grammars to construct the system of automated modeling of nonlinear semiconductor systems. The result of the scientific research is the logical-mathematical model using the terms of functional context-free grammars. The model substantiated by a logical conclusion using lambda calculus. Practical application of the method is demonstrated by examples of solving direct and inverse modeling problems.\",\"PeriodicalId\":177690,\"journal\":{\"name\":\"2018 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FAREASTCON.2018.8602769\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FAREASTCON.2018.8602769","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Logic-Functional Modeling of Nonlinear Radio Engineering Systems
This article is devoted to computer modeling of radio systems. The paper shows a method for automated solution of direct and inverse problems of mathematical modeling of nonlinear radio engineering systems. Nonlinear radio engineering systems perform important signal generation and conversion operations: amplification, modulation, detection, frequency multiplication and conversion. Modeling of such systems is necessary to improve the efficiency of their design and research. Existing modeling systems are application packages, that are limited to a set of valid tasks. They usually solve only direct problems (problems of system analysis). Modern radio engineering requires the ability to solve various direct and inverse problems of systems modeling within a uniform program complex. Therefore, the application packages should be replaced by complexes of automated modeling using elements of artificial intelligence. The article proposes a method of using functional grammars to construct the system of automated modeling of nonlinear semiconductor systems. The result of the scientific research is the logical-mathematical model using the terms of functional context-free grammars. The model substantiated by a logical conclusion using lambda calculus. Practical application of the method is demonstrated by examples of solving direct and inverse modeling problems.