{"title":"工业应用中的软计算和分形理论","authors":"O. Castillo, P. Melin","doi":"10.1109/FUZZ.2003.1206655","DOIUrl":null,"url":null,"abstract":"This tutorial will show how to use different Soil Computing (SC) techniques for the development of hybrid intelligent systems for industrial applications. SC techniques, at the moment, include Neural Networks, Fuzzy Logic, Genetic Algorithms and Chaos Theory. We also consider the use of Fractal Theory for pattern recognition and time series analysis. Each of these methodologies has its advantages and disadvantages and many problems have been solved, by using one of these methodologies. However, many real-world complex industrial problems require the integration of several of these methodologies to really achieve the efficiency and accuracy needed in practice. In this tutorial a brief introduction to SC methodologies will be given. Then, different methods for integrating the different SC methodologies in solving real-world problems will be described. At the end, the integration methodologies will he illustrated with real hybrid intelligent systems that have been developed for applications like: Food Processing Plants, Robotic Systems, Automated Quality Control, Financial and Economic Forecasting, and Manufacturing Systems, Those attending can expect to gain awareness of the role of SC methodologies and their integration in solving real world complex problems.","PeriodicalId":212172,"journal":{"name":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Soft computing and fractal theory for industrial applications\",\"authors\":\"O. Castillo, P. Melin\",\"doi\":\"10.1109/FUZZ.2003.1206655\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This tutorial will show how to use different Soil Computing (SC) techniques for the development of hybrid intelligent systems for industrial applications. SC techniques, at the moment, include Neural Networks, Fuzzy Logic, Genetic Algorithms and Chaos Theory. We also consider the use of Fractal Theory for pattern recognition and time series analysis. Each of these methodologies has its advantages and disadvantages and many problems have been solved, by using one of these methodologies. However, many real-world complex industrial problems require the integration of several of these methodologies to really achieve the efficiency and accuracy needed in practice. In this tutorial a brief introduction to SC methodologies will be given. Then, different methods for integrating the different SC methodologies in solving real-world problems will be described. At the end, the integration methodologies will he illustrated with real hybrid intelligent systems that have been developed for applications like: Food Processing Plants, Robotic Systems, Automated Quality Control, Financial and Economic Forecasting, and Manufacturing Systems, Those attending can expect to gain awareness of the role of SC methodologies and their integration in solving real world complex problems.\",\"PeriodicalId\":212172,\"journal\":{\"name\":\"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZ.2003.1206655\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZ.2003.1206655","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Soft computing and fractal theory for industrial applications
This tutorial will show how to use different Soil Computing (SC) techniques for the development of hybrid intelligent systems for industrial applications. SC techniques, at the moment, include Neural Networks, Fuzzy Logic, Genetic Algorithms and Chaos Theory. We also consider the use of Fractal Theory for pattern recognition and time series analysis. Each of these methodologies has its advantages and disadvantages and many problems have been solved, by using one of these methodologies. However, many real-world complex industrial problems require the integration of several of these methodologies to really achieve the efficiency and accuracy needed in practice. In this tutorial a brief introduction to SC methodologies will be given. Then, different methods for integrating the different SC methodologies in solving real-world problems will be described. At the end, the integration methodologies will he illustrated with real hybrid intelligent systems that have been developed for applications like: Food Processing Plants, Robotic Systems, Automated Quality Control, Financial and Economic Forecasting, and Manufacturing Systems, Those attending can expect to gain awareness of the role of SC methodologies and their integration in solving real world complex problems.