Pub Date : 2012-08-31DOI: 10.1109/NAFIPS.2012.6291049
O. Arias-Enriquez, M. I. Chacon-Murguia, R. Sandoval-Rodriguez
Development of human gait analysis systems has become of great interest in the medical field because of their capacity to acquire information and perform diagnosis. This paper presents the design of a fuzzy system able to provide a linguistic interpretation of the kinematic analysis for the thigh and knee in the sagittal plane. This analysis allows the detection of anomalies like hyperflexion, excessive flexion, inadequate flexion, inadequate extension, slight extension, excessive extension or normal angular movements during the gait cycle phases. The fuzzy system is based on the mapping of reported normal behavior curves to fuzzy rules. The fuzzy system is expected to work directly with information generated by 2D or 3D vision systems. Tests on pathological as well as normal cases for thigh and knee showed correct analysis by the fuzzy system.
{"title":"Kinematic analysis of gait cycle using a fuzzy system for medical diagnosis","authors":"O. Arias-Enriquez, M. I. Chacon-Murguia, R. Sandoval-Rodriguez","doi":"10.1109/NAFIPS.2012.6291049","DOIUrl":"https://doi.org/10.1109/NAFIPS.2012.6291049","url":null,"abstract":"Development of human gait analysis systems has become of great interest in the medical field because of their capacity to acquire information and perform diagnosis. This paper presents the design of a fuzzy system able to provide a linguistic interpretation of the kinematic analysis for the thigh and knee in the sagittal plane. This analysis allows the detection of anomalies like hyperflexion, excessive flexion, inadequate flexion, inadequate extension, slight extension, excessive extension or normal angular movements during the gait cycle phases. The fuzzy system is based on the mapping of reported normal behavior curves to fuzzy rules. The fuzzy system is expected to work directly with information generated by 2D or 3D vision systems. Tests on pathological as well as normal cases for thigh and knee showed correct analysis by the fuzzy system.","PeriodicalId":281863,"journal":{"name":"2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115160341","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 : 2012-08-31DOI: 10.1109/NAFIPS.2012.6290986
B. Bede, I. Rudas
The present paper is intended to move one step forward regarding research in the area of two-point boundary value problems for fuzzy differential equations. We study the proposed problem first from theoretical, then from practical points of view. From the theoretical point of view, we prove an existence theorem of a solution inferred from a related fuzzy integral equation. From the practical point of view, we propose a shooting algorithm for numerically solving fuzzy two-point boundary value problems as for example a fuzzy elastica problem.
{"title":"Shooting method for fuzzy two-point boundary value problems","authors":"B. Bede, I. Rudas","doi":"10.1109/NAFIPS.2012.6290986","DOIUrl":"https://doi.org/10.1109/NAFIPS.2012.6290986","url":null,"abstract":"The present paper is intended to move one step forward regarding research in the area of two-point boundary value problems for fuzzy differential equations. We study the proposed problem first from theoretical, then from practical points of view. From the theoretical point of view, we prove an existence theorem of a solution inferred from a related fuzzy integral equation. From the practical point of view, we propose a shooting algorithm for numerically solving fuzzy two-point boundary value problems as for example a fuzzy elastica problem.","PeriodicalId":281863,"journal":{"name":"2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127364580","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 : 2012-08-31DOI: 10.1109/NAFIPS.2012.6291016
V. Cross, A. Chennai-Thiagarajan
Numerous ontological or semantic similarity measures have been proposed to determine how similar one concept is to another within the context of an ontology. One category of such measures uses a measure of information content (IC) for ontological concepts. The various approaches to determining IC measures are reviewed. The early corpus-based IC measure is first presented and its weakness discussed. Then the earliest ontology-based IC measure using decedents only is described followed by the two more recent proposals that attempt to improve on the original descendents only ontology-based IC measure. These three IC measures are then analyzed and critiqued based on the assumptions made during their construction. Analysis of the strengths and weakness of the three proposed IC measures suggest intuitive principles to be followed in construction of other IC measures.
{"title":"Measuring information content for an ontological concept","authors":"V. Cross, A. Chennai-Thiagarajan","doi":"10.1109/NAFIPS.2012.6291016","DOIUrl":"https://doi.org/10.1109/NAFIPS.2012.6291016","url":null,"abstract":"Numerous ontological or semantic similarity measures have been proposed to determine how similar one concept is to another within the context of an ontology. One category of such measures uses a measure of information content (IC) for ontological concepts. The various approaches to determining IC measures are reviewed. The early corpus-based IC measure is first presented and its weakness discussed. Then the earliest ontology-based IC measure using decedents only is described followed by the two more recent proposals that attempt to improve on the original descendents only ontology-based IC measure. These three IC measures are then analyzed and critiqued based on the assumptions made during their construction. Analysis of the strengths and weakness of the three proposed IC measures suggest intuitive principles to be followed in construction of other IC measures.","PeriodicalId":281863,"journal":{"name":"2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114494285","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 : 2012-08-31DOI: 10.1109/NAFIPS.2012.6291026
M. Korjani, J. Mendel
Fuzzy Set Qualitative Comparative Analysis (fsQCA) is a methodology for obtaining linguistic summarizations from data that are associated with cases. It was developed by the social scientist Prof. Charles C. Ragin. fsQCA seeks to establish logical connections between combinations of causal conditions and an outcome, the result being rules that describe how combinations of causal conditions would cause the desired outcome. So, each rule is a possible path from the causal conditions to the outcome. The rules are connected by the word OR to the output. To actually apply fsQCA to some engineering data problems, there are some challenges that had to be overcome. We explain the challenges and how they have been overcome. We also illustrate the application of fsQCA to the well-known Auto MPG dataset to obtain causal combinations that explain Low MPG 4-cylinder cars.
模糊集定性比较分析(fsQCA)是一种从与案例相关的数据中获得语言摘要的方法。它是由社会科学家Charles C. Ragin教授开发的。fsQCA试图在因果条件的组合和结果之间建立逻辑联系，结果是描述因果条件的组合如何导致预期结果的规则。所以，每条规则都是一条从因果条件到结果的可能路径。规则通过单词OR连接到输出。要将fsQCA实际应用于一些工程数据问题，必须克服一些挑战。我们将解释这些挑战以及如何克服这些挑战。我们还说明了fsQCA在著名的Auto MPG数据集上的应用，以获得解释低MPG 4缸汽车的因果组合。
{"title":"Fuzzy set Qualitative Comparative Analysis (fsQCA): Challenges and applications","authors":"M. Korjani, J. Mendel","doi":"10.1109/NAFIPS.2012.6291026","DOIUrl":"https://doi.org/10.1109/NAFIPS.2012.6291026","url":null,"abstract":"Fuzzy Set Qualitative Comparative Analysis (fsQCA) is a methodology for obtaining linguistic summarizations from data that are associated with cases. It was developed by the social scientist Prof. Charles C. Ragin. fsQCA seeks to establish logical connections between combinations of causal conditions and an outcome, the result being rules that describe how combinations of causal conditions would cause the desired outcome. So, each rule is a possible path from the causal conditions to the outcome. The rules are connected by the word OR to the output. To actually apply fsQCA to some engineering data problems, there are some challenges that had to be overcome. We explain the challenges and how they have been overcome. We also illustrate the application of fsQCA to the well-known Auto MPG dataset to obtain causal combinations that explain Low MPG 4-cylinder cars.","PeriodicalId":281863,"journal":{"name":"2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122174739","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 : 2012-08-31DOI: 10.1109/NAFIPS.2012.6291007
C. E. Celemin, M. Melgarejo
This paper presents a new iterative algorithm for computing the centroid of an interval type-2 fuzzy set (also known as interval-valued fuzzy set). The algorithm uses precomputation to speed up its calculations and an initialization based on the concept of uncertainty bounds. Computational experiments over different kind of footprints of uncertainty and several discretization levels show that the new algorithm is much faster than methods such as the Enhanced Karnik-Mendel Algorithm (EKMA) and the Enhanced Iterative Algorithm with Stop Conditon (EIASC).
{"title":"A faster iterative computation of the centroid of an interval type-2 fuzzy set","authors":"C. E. Celemin, M. Melgarejo","doi":"10.1109/NAFIPS.2012.6291007","DOIUrl":"https://doi.org/10.1109/NAFIPS.2012.6291007","url":null,"abstract":"This paper presents a new iterative algorithm for computing the centroid of an interval type-2 fuzzy set (also known as interval-valued fuzzy set). The algorithm uses precomputation to speed up its calculations and an initialization based on the concept of uncertainty bounds. Computational experiments over different kind of footprints of uncertainty and several discretization levels show that the new algorithm is much faster than methods such as the Enhanced Karnik-Mendel Algorithm (EKMA) and the Enhanced Iterative Algorithm with Stop Conditon (EIASC).","PeriodicalId":281863,"journal":{"name":"2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117022892","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 : 2012-08-31DOI: 10.1109/NAFIPS.2012.6290971
M. Zarandi, S. Sedehizadeh, I. Turksen
After more than three decades since the introduction of linguistic variables and their application to approximate reasoning by Zadeh [1], the ability of fuzzy logic systems (FLSs) for modeling real world applications is not a secret to anyone. Currently there are two basic approaches to determine fuzzy model of a system in the literature which are, 1-direct approach, and 2-indirect approach. In direct approach rules are generated via knowledge extraction from experienced experts, while in indirect approach historical data of a system determine the governing rules. The first method is involved with extracting knowledge from experts who in some cases are not available, or they avoid providing us with useful information. In the second method which is dealt with historical data, clustering is the proper tool for structure identification of a system under investigation. Determining the structure of a system relying only on past data also has its own problems. In this paper we try to develop a hybrid approach in interval type-2 fuzzy system modeling (IT2FSM) which benefits from the advantages of both direct and indirect methods. At first stage the modified approach to interval type-2 fuzzy c-mean clustering (IT2FCM) is applied to identify the structure of system and in the second stage the hybrid of direct and indirect approach in system modeling is used to complete the rule base of a model.
{"title":"A hybrid approach to develop an interval type-2 fuzzy logic system","authors":"M. Zarandi, S. Sedehizadeh, I. Turksen","doi":"10.1109/NAFIPS.2012.6290971","DOIUrl":"https://doi.org/10.1109/NAFIPS.2012.6290971","url":null,"abstract":"After more than three decades since the introduction of linguistic variables and their application to approximate reasoning by Zadeh [1], the ability of fuzzy logic systems (FLSs) for modeling real world applications is not a secret to anyone. Currently there are two basic approaches to determine fuzzy model of a system in the literature which are, 1-direct approach, and 2-indirect approach. In direct approach rules are generated via knowledge extraction from experienced experts, while in indirect approach historical data of a system determine the governing rules. The first method is involved with extracting knowledge from experts who in some cases are not available, or they avoid providing us with useful information. In the second method which is dealt with historical data, clustering is the proper tool for structure identification of a system under investigation. Determining the structure of a system relying only on past data also has its own problems. In this paper we try to develop a hybrid approach in interval type-2 fuzzy system modeling (IT2FSM) which benefits from the advantages of both direct and indirect methods. At first stage the modified approach to interval type-2 fuzzy c-mean clustering (IT2FCM) is applied to identify the structure of system and in the second stage the hybrid of direct and indirect approach in system modeling is used to complete the rule base of a model.","PeriodicalId":281863,"journal":{"name":"2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130618300","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 : 2012-08-31DOI: 10.1109/NAFIPS.2012.6291006
M. J. Wierman, T. Clark, J. Mordeson, W. J. Tastle
The rational choice model developed for economics has been adopted by the political science communtity. Unfortunately the rational choice model does not seem to be as applicable to political situations as it is to economic situations. Reasonable models lead to undesirable conclusions, as exemplified by Arrows theorem, which uses for reasonable axioms to conclude that dictatorship is inevitable. Of course, the axioms used have come under comprehensive analysis, and weaknesses have been shown, however, none of the axioms are blatantly unrealistic. Most are quite reasonable. Researchers have tried using fuzzy rational choice functions to get around Arrow, to produce a model of choice that is reasonable but non-dictatorial. However, the methodology used is often straightforward fuzzification of crisp concepts. This paper argues that these concepts really do not translate as well as one would like.
{"title":"A critique of fuzzy rational choice models","authors":"M. J. Wierman, T. Clark, J. Mordeson, W. J. Tastle","doi":"10.1109/NAFIPS.2012.6291006","DOIUrl":"https://doi.org/10.1109/NAFIPS.2012.6291006","url":null,"abstract":"The rational choice model developed for economics has been adopted by the political science communtity. Unfortunately the rational choice model does not seem to be as applicable to political situations as it is to economic situations. Reasonable models lead to undesirable conclusions, as exemplified by Arrows theorem, which uses for reasonable axioms to conclude that dictatorship is inevitable. Of course, the axioms used have come under comprehensive analysis, and weaknesses have been shown, however, none of the axioms are blatantly unrealistic. Most are quite reasonable. Researchers have tried using fuzzy rational choice functions to get around Arrow, to produce a model of choice that is reasonable but non-dictatorial. However, the methodology used is often straightforward fuzzification of crisp concepts. This paper argues that these concepts really do not translate as well as one would like.","PeriodicalId":281863,"journal":{"name":"2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130654445","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 : 2012-08-31DOI: 10.1109/NAFIPS.2012.6290980
Juan Paulo Alvarado-Magaña, Antonio Rodríguez-Díaz, J. R. Castro, O. Castillo
Many computer simulations try to explain language evolution with socially oriented scenarios in multi-agent systems by applying methods such as genetic algorithms and neural networks. In this paper a new approach based on modifications to the classic Ant Colony Optimization algorithm is proposed. Ants are provided with a Fuzzy Grammar and the ability to embed a message in the pheromone. By fuzzifying the grammar, ants are able to modify the degree of membership of the production rules to gradually adopt a foreign language.
{"title":"Simulation of language evolution using Fuzzy Grammars","authors":"Juan Paulo Alvarado-Magaña, Antonio Rodríguez-Díaz, J. R. Castro, O. Castillo","doi":"10.1109/NAFIPS.2012.6290980","DOIUrl":"https://doi.org/10.1109/NAFIPS.2012.6290980","url":null,"abstract":"Many computer simulations try to explain language evolution with socially oriented scenarios in multi-agent systems by applying methods such as genetic algorithms and neural networks. In this paper a new approach based on modifications to the classic Ant Colony Optimization algorithm is proposed. Ants are provided with a Fuzzy Grammar and the ability to embed a message in the pheromone. By fuzzifying the grammar, ants are able to modify the degree of membership of the production rules to gradually adopt a foreign language.","PeriodicalId":281863,"journal":{"name":"2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123861652","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 : 2012-08-31DOI: 10.1109/NAFIPS.2012.6291001
J. Mattila
An algebra of the theory of standard fuzzy sets are considered and a definition for Zadeh algebra is constructed. Basic things of the semantics of Łukasiewicz many-valued logics are derived from Zadeh algebra.
{"title":"Zadeh algebra as the basis of Łukasiewicz logics","authors":"J. Mattila","doi":"10.1109/NAFIPS.2012.6291001","DOIUrl":"https://doi.org/10.1109/NAFIPS.2012.6291001","url":null,"abstract":"An algebra of the theory of standard fuzzy sets are considered and a definition for Zadeh algebra is constructed. Basic things of the semantics of Łukasiewicz many-valued logics are derived from Zadeh algebra.","PeriodicalId":281863,"journal":{"name":"2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123972234","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 : 2012-08-31DOI: 10.1109/NAFIPS.2012.6290983
A. K. Verma, A. Srividya, A. Goyal, P. Ramesh
Machinery Health Monitoring (MHM) is increasingly being adopted by industries not only as a means for asset management but also for ensuring high levels of availability with its consequent gains. However, investments on MHM strategies will be effective only to the extent that they are appropriately selected and utilized. As a part of the process for selection of MHM systems, it is necessary to ascertain the impact of MHM systems in enhancing availability at plant level and in a cost effective manner. In this paper the need to quantify features of MHM systems such as detectability and prognostic ability and a simple fuzzy logic based method for doing so are discussed. These features are then incorporated in a multi-objective maintenance optimisation model based on Markov process and genetic algorithm. The results of the optimisation serve as decision support for selection of MHM systems. A case study is presented to demonstrate the concept.
{"title":"Application of fuzzy logic for selection of Machinery Health Monitoring strategies","authors":"A. K. Verma, A. Srividya, A. Goyal, P. Ramesh","doi":"10.1109/NAFIPS.2012.6290983","DOIUrl":"https://doi.org/10.1109/NAFIPS.2012.6290983","url":null,"abstract":"Machinery Health Monitoring (MHM) is increasingly being adopted by industries not only as a means for asset management but also for ensuring high levels of availability with its consequent gains. However, investments on MHM strategies will be effective only to the extent that they are appropriately selected and utilized. As a part of the process for selection of MHM systems, it is necessary to ascertain the impact of MHM systems in enhancing availability at plant level and in a cost effective manner. In this paper the need to quantify features of MHM systems such as detectability and prognostic ability and a simple fuzzy logic based method for doing so are discussed. These features are then incorporated in a multi-objective maintenance optimisation model based on Markov process and genetic algorithm. The results of the optimisation serve as decision support for selection of MHM systems. A case study is presented to demonstrate the concept.","PeriodicalId":281863,"journal":{"name":"2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121181503","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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