Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226835
A. del Amo, D. Gómez, J. Montero
The goal of this paper is to present an algorithm for terrain matching, leveraging an existing fuzzy clustering algorithm, and modifying it to its supervised version, in order to apply the algorithm to georegistration and, later on pattern recognition. Georegistration is the process of adjusting one drawing or image to the geographic location of a "known good" reference drawing, image, surface or map, The georegistration problem can be treated as a pattern recognition problem; and it can be applied to the target detection problem. The terrain matching algorithm will be based on fuzzy set theory as a very accurate method to represent the imprecision of the real world, and presented as a multicriteria decision making problem. The energy emitted and reflected by the Earth's surface has to be recorded by relatively complex remote sensing devices that have spatial, spectral and geometrical resolution constraints. Errors usually slip into the data acquisition process. Therefore, it is necessary to preprocess the remotely sensed data, prior to analyzing it (image restoration, involving the correction of distortion, degradation and noise introduced during the rendering process). In this paper we shall assume that all these problems have been solved, focusing our study on the image classification of a corrected image being close enough, both geometrically and radiometrically, to the radiant energy characteristics of the target scene. In particular, at a first stage we consider each pixel individually; and a class will be assigned to each pixel, taking into account several values measured in separate spectral bands. Then we shall describe an automatic detection system based on a previous algorithm developed in A. Del Amo et al., introducing now the fuzzy partition model proposed by A. Del Amo et al. A first phase will lead to a spectral definition of patterns; and a second phase will lead to classification and recognition. Similarity measures will then allow us to evaluate the degree to which a pixel can be associated to each pattern, or determine if a pattern is similar enough to a subimage of the main image, to establish that a target we are looking for can be found on that image.
本文的目标是提出一种地形匹配算法，利用现有的模糊聚类算法，并将其修改为其监督版本，以便将该算法应用于地理配准以及后来的模式识别。地理配准是将一幅图或图像调整到一幅“已知好的”参考图、图像、曲面或地图的地理位置的过程。地理配准问题可视为模式识别问题;该方法可以应用于目标检测问题。地形匹配算法将基于模糊集理论作为一种非常精确的方法来表示现实世界的不精确性，并以多准则决策问题的形式呈现。地球表面发射和反射的能量必须由相对复杂的遥感设备记录，这些设备在空间、光谱和几何分辨率方面受到限制。数据采集过程中通常会出现错误。因此，在对遥感数据进行分析之前，有必要对其进行预处理(图像恢复，包括对绘制过程中引入的失真、退化和噪声的校正)。在本文中，我们假设所有这些问题都已经解决，重点研究在几何和辐射上足够接近目标场景的辐射能量特征的校正图像的图像分类。特别是，在第一阶段，我们单独考虑每个像素;考虑到在不同的光谱波段测量的几个值，将为每个像素分配一个类。然后，我们将描述一个基于a . Del Amo等人先前开发的算法的自动检测系统，现在介绍a . Del Amo等人提出的模糊划分模型。第一阶段将导致模式的谱定义;第二阶段是分类和识别。然后，相似性度量将允许我们评估像素与每个模式的关联程度，或者确定模式是否与主图像的子图像足够相似，以确定我们正在寻找的目标可以在该图像上找到。
{"title":"Spectral fuzzy classification system for target recognition","authors":"A. del Amo, D. Gómez, J. Montero","doi":"10.1109/NAFIPS.2003.1226835","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226835","url":null,"abstract":"The goal of this paper is to present an algorithm for terrain matching, leveraging an existing fuzzy clustering algorithm, and modifying it to its supervised version, in order to apply the algorithm to georegistration and, later on pattern recognition. Georegistration is the process of adjusting one drawing or image to the geographic location of a \"known good\" reference drawing, image, surface or map, The georegistration problem can be treated as a pattern recognition problem; and it can be applied to the target detection problem. The terrain matching algorithm will be based on fuzzy set theory as a very accurate method to represent the imprecision of the real world, and presented as a multicriteria decision making problem. The energy emitted and reflected by the Earth's surface has to be recorded by relatively complex remote sensing devices that have spatial, spectral and geometrical resolution constraints. Errors usually slip into the data acquisition process. Therefore, it is necessary to preprocess the remotely sensed data, prior to analyzing it (image restoration, involving the correction of distortion, degradation and noise introduced during the rendering process). In this paper we shall assume that all these problems have been solved, focusing our study on the image classification of a corrected image being close enough, both geometrically and radiometrically, to the radiant energy characteristics of the target scene. In particular, at a first stage we consider each pixel individually; and a class will be assigned to each pixel, taking into account several values measured in separate spectral bands. Then we shall describe an automatic detection system based on a previous algorithm developed in A. Del Amo et al., introducing now the fuzzy partition model proposed by A. Del Amo et al. A first phase will lead to a spectral definition of patterns; and a second phase will lead to classification and recognition. Similarity measures will then allow us to evaluate the degree to which a pixel can be associated to each pattern, or determine if a pattern is similar enough to a subimage of the main image, to establish that a target we are looking for can be found on that image.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"89 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":"116237462","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.2003.1226775
R. Intan, Masao Mukaidono
This paper discusses preciseness of data in terms of obtaining degree of similarity in which a fuzzy set can be used as an alternative to represent imprecise data. Degree of similarity between two imprecise data represented in two fuzzy sets is approximately determined by using a fuzzy conditional probability relation. More-over, the degree of similarity relationship between fuzzy sets corresponding to fuzzy classes as results of a fuzzy partition on a given finite set of data is examined. Related to a well known fuzzy partition, called fuzzy pseudopartition or fuzzy c-partition where c designates the number of fuzzy classes in the partition, we introduced fuzzy symmetric c-partition regarded as a special case of the fuzzy c-partition. In addition, we also introduced fuzzy covering as a generalization of fuzzy partition. Similarly, two fuzzy coverings, namely fuzzy c-covering and fuzzy symmetric c-covering are proposed corresponding to the fuzzy c-partition and the fuzzy symmetric c-partition, respectively. In this paper, special attention will be given to apply the concept of fuzzy c-covering in generating a fuzzy thesaurus.
{"title":"A proposal of fuzzy thesaurus generated by fuzzy covering","authors":"R. Intan, Masao Mukaidono","doi":"10.1109/NAFIPS.2003.1226775","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226775","url":null,"abstract":"This paper discusses preciseness of data in terms of obtaining degree of similarity in which a fuzzy set can be used as an alternative to represent imprecise data. Degree of similarity between two imprecise data represented in two fuzzy sets is approximately determined by using a fuzzy conditional probability relation. More-over, the degree of similarity relationship between fuzzy sets corresponding to fuzzy classes as results of a fuzzy partition on a given finite set of data is examined. Related to a well known fuzzy partition, called fuzzy pseudopartition or fuzzy c-partition where c designates the number of fuzzy classes in the partition, we introduced fuzzy symmetric c-partition regarded as a special case of the fuzzy c-partition. In addition, we also introduced fuzzy covering as a generalization of fuzzy partition. Similarly, two fuzzy coverings, namely fuzzy c-covering and fuzzy symmetric c-covering are proposed corresponding to the fuzzy c-partition and the fuzzy symmetric c-partition, respectively. In this paper, special attention will be given to apply the concept of fuzzy c-covering in generating a fuzzy thesaurus.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"14 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":"125038322","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.2003.1226761
T. R. Gabriel, M. Berthold
Many fuzzy rule induction algorithms have been proposed in the past. Most of them tend to generate too many rides during the learning process. This is due to data sets obtained from real world systems containing distorted elements or noisy data. Most approaches try to completely ignore outliers, which can be potentially harmful since the example may describe a rare but still extremely interesting phenomena in the data. In order to avoid this conflict, we propose to build a hierarchy of fuzzy rule systems. The goal of this model-hierarchy are interpretable models with only few relevant rules on each level of the hierarchy. The resulting fuzzy model hierarchy forms a structure in which the top model covers all data explicitly and generates a significant smaller number of rules than the original fuzzy rule learner. The models on the bottom, on the other hand, consist of only a few rules in each level and explain pans with only weak relevance in the data. We demonstrate the proposed method's usefulness on several classification benchmark data sets. The results demonstrate how the rule hierarchy allows to build much smaller fuzzy rule systems and how the model-especially at higher levels of the hierarchy-remains interpretable.
{"title":"Formation of hierarchical fuzzy rule systems","authors":"T. R. Gabriel, M. Berthold","doi":"10.1109/NAFIPS.2003.1226761","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226761","url":null,"abstract":"Many fuzzy rule induction algorithms have been proposed in the past. Most of them tend to generate too many rides during the learning process. This is due to data sets obtained from real world systems containing distorted elements or noisy data. Most approaches try to completely ignore outliers, which can be potentially harmful since the example may describe a rare but still extremely interesting phenomena in the data. In order to avoid this conflict, we propose to build a hierarchy of fuzzy rule systems. The goal of this model-hierarchy are interpretable models with only few relevant rules on each level of the hierarchy. The resulting fuzzy model hierarchy forms a structure in which the top model covers all data explicitly and generates a significant smaller number of rules than the original fuzzy rule learner. The models on the bottom, on the other hand, consist of only a few rules in each level and explain pans with only weak relevance in the data. We demonstrate the proposed method's usefulness on several classification benchmark data sets. The results demonstrate how the rule hierarchy allows to build much smaller fuzzy rule systems and how the model-especially at higher levels of the hierarchy-remains interpretable.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"21 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":"125562477","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.2003.1226840
K. Menon, C. Dagli
Different users have different needs from the same web page and hence it is necessary to develop a system which understands the needs and demands of the users. Web server logs have abundant information about the nature of users accessing it. In this paper we discussed how to mine these web server logs for a given period of time using unsupervised and competitive learning algorithm like Kohonen's self organizing maps (SOM) and interpreting those results using Unified distance Matrix (U-matrix). These algorithms help us in efficiently clustering users based on similar web access patterns and each cluster having users with similar browsing patterns. These clusters are useful in web personalization so that it communicates better with its users and also in web traffic analysis for predicting web traffic at a given period of time.
{"title":"Web personalization using neuro-fuzzy clustering algorithms","authors":"K. Menon, C. Dagli","doi":"10.1109/NAFIPS.2003.1226840","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226840","url":null,"abstract":"Different users have different needs from the same web page and hence it is necessary to develop a system which understands the needs and demands of the users. Web server logs have abundant information about the nature of users accessing it. In this paper we discussed how to mine these web server logs for a given period of time using unsupervised and competitive learning algorithm like Kohonen's self organizing maps (SOM) and interpreting those results using Unified distance Matrix (U-matrix). These algorithms help us in efficiently clustering users based on similar web access patterns and each cluster having users with similar browsing patterns. These clusters are useful in web personalization so that it communicates better with its users and also in web traffic analysis for predicting web traffic at a given period of time.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"64 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":"126504636","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.2003.1226771
S.H. Kim, H. Tizhoosh, M. Kamel
Template matching algorithms determine the best matching position of a reference image (template) on a larger image (scene) in either complete or incomplete information environment. In this work, our main objective is to devise a fuzzy integral-based aggregation scheme in an attempt to get more accurate and robust matching, by combining the matching decisions of a finite number of image template matching algorithms, Particularly, Choquet integrals associated with fuzzy measures can be used for handling fuzziness due to incomplete image information. In the present work, a fuzzy integral-based aggregated template matching system is developed on the basis of Choquet integral using belief, plausibility, and probability measure, while being interpreted as an optimistic, a pessimistic, and a noninteracting aggregation, respectively. Finally, to show a validation of Choquet integral-based template matching methods, three individual template matching methods (i,e., MOAD-matcher, SOAD-matcher, and SOSD-matcher) are combined using Choquet integral with respect to different fuzzy measures. Then, performance of these aggregated matchers is compared to individual matchers' performance. It is found that in a complementary sense a Choquet integral-based aggregation of template matching methods gives a better performance compared to the performance of the individual methods.
{"title":"Choquet integral-based aggregation of image template matching algorithms","authors":"S.H. Kim, H. Tizhoosh, M. Kamel","doi":"10.1109/NAFIPS.2003.1226771","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226771","url":null,"abstract":"Template matching algorithms determine the best matching position of a reference image (template) on a larger image (scene) in either complete or incomplete information environment. In this work, our main objective is to devise a fuzzy integral-based aggregation scheme in an attempt to get more accurate and robust matching, by combining the matching decisions of a finite number of image template matching algorithms, Particularly, Choquet integrals associated with fuzzy measures can be used for handling fuzziness due to incomplete image information. In the present work, a fuzzy integral-based aggregated template matching system is developed on the basis of Choquet integral using belief, plausibility, and probability measure, while being interpreted as an optimistic, a pessimistic, and a noninteracting aggregation, respectively. Finally, to show a validation of Choquet integral-based template matching methods, three individual template matching methods (i,e., MOAD-matcher, SOAD-matcher, and SOSD-matcher) are combined using Choquet integral with respect to different fuzzy measures. Then, performance of these aggregated matchers is compared to individual matchers' performance. It is found that in a complementary sense a Choquet integral-based aggregation of template matching methods gives a better performance compared to the performance of the individual methods.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"7 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":"121857623","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.2003.1226765
S. Singh, K. Rattan
Implementation of a fuzzy logic controller on an FPGA using VHDL is presented in this paper. The basic components of the fuzzy logic controller are designed using VHDL and a Xilinx virtex FPGA is used for implementation. The fuzzy logic controller with an 8-bit input, 8-bit output is tested by controlling single disk of an ECP torsional plant.
{"title":"Implementation of a fuzzy logic controller on an FPGA using VHDL","authors":"S. Singh, K. Rattan","doi":"10.1109/NAFIPS.2003.1226765","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226765","url":null,"abstract":"Implementation of a fuzzy logic controller on an FPGA using VHDL is presented in this paper. The basic components of the fuzzy logic controller are designed using VHDL and a Xilinx virtex FPGA is used for implementation. The fuzzy logic controller with an 8-bit input, 8-bit output is tested by controlling single disk of an ECP torsional plant.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"23 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":"126620406","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.2003.1226772
Q. Sjahputera, J. Keller, P. Matsakis
Scene matching is the process of recognizing two images as different views of the same scene captured using different sensor poses, and/or different types of sensors. In this work, each image contains the same objects and sensor pose parameters are not known. The spatial relationships among objects in the image, calculated using the histogram of forces (F-histogram) method, are used as matching elements. The degree of matching between two matching elements is calculated by comparing their F-histogram representations. Various geometric transformations are applied to the F-histograms during the comparison process to maximize the histogram similarity measure and to estimate the sensor pose parameters. The histogram similarity measure and the estimated sensor pose parameters are used as features in finding the best histogram correspondence map that matches the two images.
{"title":"Scene matching by spatial relationships","authors":"Q. Sjahputera, J. Keller, P. Matsakis","doi":"10.1109/NAFIPS.2003.1226772","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226772","url":null,"abstract":"Scene matching is the process of recognizing two images as different views of the same scene captured using different sensor poses, and/or different types of sensors. In this work, each image contains the same objects and sensor pose parameters are not known. The spatial relationships among objects in the image, calculated using the histogram of forces (F-histogram) method, are used as matching elements. The degree of matching between two matching elements is calculated by comparing their F-histogram representations. Various geometric transformations are applied to the F-histograms during the comparison process to maximize the histogram similarity measure and to estimate the sensor pose parameters. The histogram similarity measure and the estimated sensor pose parameters are used as features in finding the best histogram correspondence map that matches the two images.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"22 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":"127166250","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.2003.1226813
M. Somodevilla, F. Petry
Topological relations among spatial objects with indeterminate boundaries constitute an active area in GIScience. This paper addresses the problem of fuzzy boundaries in particular in regions with smooth variation of their attributes. The work approach presented here allows us to model, in a more realistic way, geographic phenomena like air pollution and storm intensity. Broad fuzzy boundaries are represented as a set of the /spl alpha/-cuts nested around the minimum bounding rectangle enclosing the fuzzy region.
{"title":"Approximation of topological relations on fuzzy regions: an approach using minimal bounding rectangles","authors":"M. Somodevilla, F. Petry","doi":"10.1109/NAFIPS.2003.1226813","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226813","url":null,"abstract":"Topological relations among spatial objects with indeterminate boundaries constitute an active area in GIScience. This paper addresses the problem of fuzzy boundaries in particular in regions with smooth variation of their attributes. The work approach presented here allows us to model, in a more realistic way, geographic phenomena like air pollution and storm intensity. Broad fuzzy boundaries are represented as a set of the /spl alpha/-cuts nested around the minimum bounding rectangle enclosing the fuzzy region.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"12 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":"127761566","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.2003.1226820
C. De La Mora, P. Wojciechowski, V. Kreinovich, S. Starks, P. Tanenbaum, A. Kuzminykh
To describe the response of engineering complex systems to various damage mechanics, engineers have traditionally use number-valued utilities to describe the results of different possible outcomes, and (number-valued) probabilities (often, subjective probabilities) to describe the relative frequency of different outcomes. This description is based on the assumption that experts can always make a definite preference between two possible outcomes, i.e., that the set of all outcomes is linearly (totally) ordered. In practice, experts often cannot make a choice, their preference is only a partial order. In this paper, we describe a new approach based on partial order.
{"title":"Robust methodology for characterizing system response to damage: a subjective (fuzzy) partial ordered modification of the traditional utility-probability scheme","authors":"C. De La Mora, P. Wojciechowski, V. Kreinovich, S. Starks, P. Tanenbaum, A. Kuzminykh","doi":"10.1109/NAFIPS.2003.1226820","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226820","url":null,"abstract":"To describe the response of engineering complex systems to various damage mechanics, engineers have traditionally use number-valued utilities to describe the results of different possible outcomes, and (number-valued) probabilities (often, subjective probabilities) to describe the relative frequency of different outcomes. This description is based on the assumption that experts can always make a definite preference between two possible outcomes, i.e., that the set of all outcomes is linearly (totally) ordered. In practice, experts often cannot make a choice, their preference is only a partial order. In this paper, we describe a new approach based on partial order.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"25 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":"131435322","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.2003.1226768
Chi-Bin Cheng
The purpose of this paper is to present how a fuzzy process control chart is constructed for a process with fuzzy outcomes. In this paper, the fuzzy outcomes of the process are due to subjective quality ratings by experts. Fuzzy process control consists of an off-line stage and an on-line stage. In the off-line stage, experts give quality ratings of objects based on a numerical scale, and then these ratings are fuzzified as fuzzy numbers. Collective knowledge of experts in quality rating is acquired through fuzzy regression analysis. In the on-line stage, a computer vision system is set up to obtain the dimensions of objects, and then the fuzzy regression model maps these dimensions to fuzzy quality ratings in the form of fuzzy numbers. Finally, these fuzzy quality ratings are plotted on the fuzzy control chart. Out-of-control conditions are formulated based on possibility theory. This fuzzy control chart is analog to the x~ and R charts in statistical process control.
{"title":"Fuzzy process control based on fuzzy regression and possibility measures","authors":"Chi-Bin Cheng","doi":"10.1109/NAFIPS.2003.1226768","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226768","url":null,"abstract":"The purpose of this paper is to present how a fuzzy process control chart is constructed for a process with fuzzy outcomes. In this paper, the fuzzy outcomes of the process are due to subjective quality ratings by experts. Fuzzy process control consists of an off-line stage and an on-line stage. In the off-line stage, experts give quality ratings of objects based on a numerical scale, and then these ratings are fuzzified as fuzzy numbers. Collective knowledge of experts in quality rating is acquired through fuzzy regression analysis. In the on-line stage, a computer vision system is set up to obtain the dimensions of objects, and then the fuzzy regression model maps these dimensions to fuzzy quality ratings in the form of fuzzy numbers. Finally, these fuzzy quality ratings are plotted on the fuzzy control chart. Out-of-control conditions are formulated based on possibility theory. This fuzzy control chart is analog to the x~ and R charts in statistical process control.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"2010 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":"125631339","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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