Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226791
M. Zarandi, A. Salehizadeh
Fuzzy system models are enriched by interval valued sets. They can better represent the expert's knowledge and are more flexible and robust in decision-making. This paper addresses several parameterized models for combination of FDCF and FCCF, fuzzy conjunctive canonical form and fuzzy disjunctive canonical form. Then the optimal parameters are gained through an optimization method to assign the best membership functions for the variables of the system domain. The proposed expert system is implemented for feasibility study of the army projects. This system uses Schweizer and Sklar parametric operators for t-norm and s-norm and Yager parametric defuzzification method. Moreover, for inference mechanism, FATI and FITA are combined with a linear parameter. The presented expert system shows its superiority in comparison with type I fuzzy set in flexibility, and robustness.
{"title":"Parameterized interval valued fuzzy expert systems for feasibility study of projects","authors":"M. Zarandi, A. Salehizadeh","doi":"10.1109/NAFIPS.2003.1226791","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226791","url":null,"abstract":"Fuzzy system models are enriched by interval valued sets. They can better represent the expert's knowledge and are more flexible and robust in decision-making. This paper addresses several parameterized models for combination of FDCF and FCCF, fuzzy conjunctive canonical form and fuzzy disjunctive canonical form. Then the optimal parameters are gained through an optimization method to assign the best membership functions for the variables of the system domain. The proposed expert system is implemented for feasibility study of the army projects. This system uses Schweizer and Sklar parametric operators for t-norm and s-norm and Yager parametric defuzzification method. Moreover, for inference mechanism, FATI and FITA are combined with a linear parameter. The presented expert system shows its superiority in comparison with type I fuzzy set in flexibility, and robustness.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128994318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226758
Zou Kai Qi
In system science, the unifying performance of systems is usually presented, i.e. not only study the system itself is studied, also the relation between systems is studied. This paper put forward a new algebra construction - the category of fuzzy symmetric group, by mixing classical symmetric group and fuzzy symmetric group together. On the ground of category, the method of unifying description in system science is presented.
{"title":"Fuzzy symmetric group categories in system sciences","authors":"Zou Kai Qi","doi":"10.1109/NAFIPS.2003.1226758","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226758","url":null,"abstract":"In system science, the unifying performance of systems is usually presented, i.e. not only study the system itself is studied, also the relation between systems is studied. This paper put forward a new algebra construction - the category of fuzzy symmetric group, by mixing classical symmetric group and fuzzy symmetric group together. On the ground of category, the method of unifying description in system science is presented.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121630322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-08-07DOI: 10.1109/NAFIPS.2003.1226807
K. Emami, M. Akbarzadeh-T.
A discrete-time adaptive fuzzy sliding mode controller is proposed for an antilock braking system (ABS) of a 13th order two-wheel nonlinear model of a car. The model includes interaction of front and rear wheel subsystems. The presented controller aims to least depend on a mathematical model, only assuming certain upper and lower bounds of uncertainties. The controller is designed based on a hybrid combination of variable structure control, direct adaptive fuzzy control and linear control. Two fuzzy approximators are used to estimate nonlinear functions of the plant. The controller is global uniform Lyapunov stable. Simulation results show favorable output tracking performance despite poor knowledge of wheel dynamics and system disturbances such as road roughness.
{"title":"Adaptive discrete-time fuzzy sliding mode control for anti-lock braking systems","authors":"K. Emami, M. Akbarzadeh-T.","doi":"10.1109/NAFIPS.2003.1226807","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226807","url":null,"abstract":"A discrete-time adaptive fuzzy sliding mode controller is proposed for an antilock braking system (ABS) of a 13th order two-wheel nonlinear model of a car. The model includes interaction of front and rear wheel subsystems. The presented controller aims to least depend on a mathematical model, only assuming certain upper and lower bounds of uncertainties. The controller is designed based on a hybrid combination of variable structure control, direct adaptive fuzzy control and linear control. Two fuzzy approximators are used to estimate nonlinear functions of the plant. The controller is global uniform Lyapunov stable. Simulation results show favorable output tracking performance despite poor knowledge of wheel dynamics and system disturbances such as road roughness.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117207108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"NAFIPS'2003. 22nd International Conference of the North American Fuzzy Information Processing Society - NAFIPS Proceedings (Cat. No.03TH8693)","authors":"","doi":"10.1109/NAFIPS.2003.1226745","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226745","url":null,"abstract":"The following topics are dealt with: intelligent control; manipulators; fuzzy-neuro system; fuzzy inference system; fuzzy logic controllers; fuzzy expert systems; sensitivity analysis for data mining; autonomous robot systems.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133530781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/NAFIPS.2003.1226832
C. Janikow
In the past, we have developed and presented a Fuzzy Decision Tree, more recently followed by an extension called a Fuzzy Decision Forest. The idea behind the forest is not only to represent multiple trees, but also to represent test alternatives at all levels of every tree. The resulting tree is in fact a 3-dimensional tree. A two-dimensional slice is equivalent to a single decision tree. The forest allows multiple choices of tests in some or all nodes of the decision tree. These alternative tests can be used to enhance the classification accuracy of the tree. However, the major advantage of having multiple test choices is to have alternative test decisions when features in test data are unreliable or just missing. In the paper, we overview the ideas behind Fuzzy Decision Forest, and we illustrate its enhanced capabilities with a number of experiments with missing features.
{"title":"Fuzzy Decision Forest","authors":"C. Janikow","doi":"10.1109/NAFIPS.2003.1226832","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226832","url":null,"abstract":"In the past, we have developed and presented a Fuzzy Decision Tree, more recently followed by an extension called a Fuzzy Decision Forest. The idea behind the forest is not only to represent multiple trees, but also to represent test alternatives at all levels of every tree. The resulting tree is in fact a 3-dimensional tree. A two-dimensional slice is equivalent to a single decision tree. The forest allows multiple choices of tests in some or all nodes of the decision tree. These alternative tests can be used to enhance the classification accuracy of the tree. However, the major advantage of having multiple test choices is to have alternative test decisions when features in test data are unreliable or just missing. In the paper, we overview the ideas behind Fuzzy Decision Forest, and we illustrate its enhanced capabilities with a number of experiments with missing features.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124085685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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