Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226752
P. Melin, O. Castillo
We describe in this paper the application of type-2 fuzzy logic to the problem of automated quality control in sound speaker manufacturing. Traditional quality control has been done by manually checking the quality of sound after production. This manual checking of the speakers is time consuming and occasionally was the cause of error in quality evaluation. For this reason, we developed an intelligent system for automated quality control in sound speaker manufacturing. The intelligent system has a type-2 fuzzy rule base containing the knowledge of human experts in quality control. The parameters of the fuzzy system are tuned by applying neural networks using, as training data, a real time series of measured sounds as given by good sound speakers. We also use the fractal dimension as a measure of the complexity of the sound signal.
{"title":"A new approach for quality control of sound speakers combining type-2 fuzzy logic and the fractal dimension","authors":"P. Melin, O. Castillo","doi":"10.1109/NAFIPS.2003.1226752","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226752","url":null,"abstract":"We describe in this paper the application of type-2 fuzzy logic to the problem of automated quality control in sound speaker manufacturing. Traditional quality control has been done by manually checking the quality of sound after production. This manual checking of the speakers is time consuming and occasionally was the cause of error in quality evaluation. For this reason, we developed an intelligent system for automated quality control in sound speaker manufacturing. The intelligent system has a type-2 fuzzy rule base containing the knowledge of human experts in quality control. The parameters of the fuzzy system are tuned by applying neural networks using, as training data, a real time series of measured sounds as given by good sound speakers. We also use the fractal dimension as a measure of the complexity of the sound signal.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"42 2 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":"124939377","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.2003.1226774
P. Bosc, O. Pivert
This paper is situated in the area of possibilistic relational databases, i.e., where some attribute values are imprecise and represented as possibility distributions. Any such database has a canonical interpretation as a set of regular relational databases, called worlds. This view provides the basic semantics of any query addressed to a possibilistic database. However, a query cannot be run this way for tractability reasons. This situation has led us to consider specific families of queries that can be processed in a compact way, i.e., directly on possibilistic relations. The queries dealt with in this paper, called necessity-based queries, are of the form: "to what extent is it certain that tuple t belongs to the result of query Q", where Q denotes a regular relational query. The major contribution of this paper is to identify the constraints over Q (in terms of algebraic operations) which must be imposed so that these queries are tractable.
{"title":"About certainty-based queries against possibilistic databases","authors":"P. Bosc, O. Pivert","doi":"10.1109/NAFIPS.2003.1226774","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226774","url":null,"abstract":"This paper is situated in the area of possibilistic relational databases, i.e., where some attribute values are imprecise and represented as possibility distributions. Any such database has a canonical interpretation as a set of regular relational databases, called worlds. This view provides the basic semantics of any query addressed to a possibilistic database. However, a query cannot be run this way for tractability reasons. This situation has led us to consider specific families of queries that can be processed in a compact way, i.e., directly on possibilistic relations. The queries dealt with in this paper, called necessity-based queries, are of the form: \"to what extent is it certain that tuple t belongs to the result of query Q\", where Q denotes a regular relational query. The major contribution of this paper is to identify the constraints over Q (in terms of algebraic operations) which must be imposed so that these queries are tractable.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"20 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":"122786694","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.2003.1226799
S. Chi, Wei-ling Peng, Pei-Tsang Wu, Mingtao Yu
The purpose of this research is to study the relationship of changes between the stock indicators and stock index in order to understand how the trend of stock index change is under the complex influence among the stock technical indicators. The proposed methodology, first of all, applies the self-organizing map (SOM) neural network to cluster the similar indicators into groups based on their similarity of moving curve within a certain period of time. To investigate the relationship between the stock index and the technical indicators within any of the groups, the fuzzy neural network (FNN) technique is employed to search for the rules about their relationships. To evaluate the performance of the SOM, the grey relationship analysis was used for the verification of how similar of the indicators which was clustered into a group. According to the results, it is clear that the capability of the SOM in clustering is confirmed. To further improve the predication accuracy, this research selected some key indicators from each of the groups as the inputs of neural network and the results completes a much better prediction accuracy than all of the previous networks.
{"title":"The study on the relationship among technical indicators and the development of stock index prediction system","authors":"S. Chi, Wei-ling Peng, Pei-Tsang Wu, Mingtao Yu","doi":"10.1109/NAFIPS.2003.1226799","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226799","url":null,"abstract":"The purpose of this research is to study the relationship of changes between the stock indicators and stock index in order to understand how the trend of stock index change is under the complex influence among the stock technical indicators. The proposed methodology, first of all, applies the self-organizing map (SOM) neural network to cluster the similar indicators into groups based on their similarity of moving curve within a certain period of time. To investigate the relationship between the stock index and the technical indicators within any of the groups, the fuzzy neural network (FNN) technique is employed to search for the rules about their relationships. To evaluate the performance of the SOM, the grey relationship analysis was used for the verification of how similar of the indicators which was clustered into a group. According to the results, it is clear that the capability of the SOM in clustering is confirmed. To further improve the predication accuracy, this research selected some key indicators from each of the groups as the inputs of neural network and the results completes a much better prediction accuracy than all of the previous networks.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"10 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":"124580983","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.2003.1226795
JingTao Yao
An important issue of data mining is how to transfer data into information, the information into action, and the action into value or profit. This paper presents a study on applying sensitivity analysis to neural network models for a particular area in data mining, interesting mining and profit mining. Applying sensitivity analysis to neural network models rather than just regression models can help us identify sensible factors that play important roles to dependent variables such as total profit in a dynamic environment.
{"title":"Sensitivity analysis for data mining","authors":"JingTao Yao","doi":"10.1109/NAFIPS.2003.1226795","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226795","url":null,"abstract":"An important issue of data mining is how to transfer data into information, the information into action, and the action into value or profit. This paper presents a study on applying sensitivity analysis to neural network models for a particular area in data mining, interesting mining and profit mining. Applying sensitivity analysis to neural network models rather than just regression models can help us identify sensible factors that play important roles to dependent variables such as total profit in a dynamic environment.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"5 3 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":"122544646","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.2003.1226748
J. Paetz
The starting point for this contribution is an adapted neuro-fuzzy system of Huber/Berthold with a set of adapted membership functions (number and shape). The heuristically adapted number and shape of the membership functions may not be the best choice, especially when considering human understandability of the adapted rules. We transform a-posteriori the number of fuzzy terms and evaluate classification performance and understandability, considering the influence of the weighting of the neuro-fuzzy units as well. Inference for the new, transformed (deduced) system is done by an expanded max-min inference strategy. For this expanded inference the influence of the neuro-fuzzy membership functions to the predefined number of fuzzy terms have to be determined. Thus, we introduce so called degradation factors. The evaluation of our inventions is done by medical data.
{"title":"Deducing fuzzy inference systems with different numbers of membership functions from a neuro-fuzzy inference system","authors":"J. Paetz","doi":"10.1109/NAFIPS.2003.1226748","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226748","url":null,"abstract":"The starting point for this contribution is an adapted neuro-fuzzy system of Huber/Berthold with a set of adapted membership functions (number and shape). The heuristically adapted number and shape of the membership functions may not be the best choice, especially when considering human understandability of the adapted rules. We transform a-posteriori the number of fuzzy terms and evaluate classification performance and understandability, considering the influence of the weighting of the neuro-fuzzy units as well. Inference for the new, transformed (deduced) system is done by an expanded max-min inference strategy. For this expanded inference the influence of the neuro-fuzzy membership functions to the predefined number of fuzzy terms have to be determined. Thus, we introduce so called degradation factors. The evaluation of our inventions is done by medical data.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"17 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":"115765090","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.2003.1226776
R. Thomopoulos, P. Bosc, P. Buche, O. Haemmerle
In previous studies, we have extended the conceptual graph model, which is a knowledge representation model belonging to the family of semantic networks, to be able to represent fuzzy values. The basic conceptual graph model has a logical interpretation in first-order logic. In this paper, we focus on the logical interpretation of the conceptual graph model extended to fuzzy values: we use logical implications stemming from fuzzy logic, so as to extend the logical interpretation of the model to fuzzy values and to comparisons between fuzzy conceptual graphs.
{"title":"Logical interpretations of fuzzy conceptual graphs","authors":"R. Thomopoulos, P. Bosc, P. Buche, O. Haemmerle","doi":"10.1109/NAFIPS.2003.1226776","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226776","url":null,"abstract":"In previous studies, we have extended the conceptual graph model, which is a knowledge representation model belonging to the family of semantic networks, to be able to represent fuzzy values. The basic conceptual graph model has a logical interpretation in first-order logic. In this paper, we focus on the logical interpretation of the conceptual graph model extended to fuzzy values: we use logical implications stemming from fuzzy logic, so as to extend the logical interpretation of the model to fuzzy values and to comparisons between fuzzy conceptual graphs.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"31 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":"117035748","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.2003.1226830
A. Klimke
The transformation method has been proposed for the simulation and analysis of systems with uncertain parameters. Here, several aspects of an efficient implementation are presented: fast processing of discretized fuzzy numbers through multi-dimensional arrays, elimination of recurring permutations, automatic decomposition of models, treatment of single occurrences of variables through interval arithmetic, and a monotonicity test based on automatic differentiation.
{"title":"An efficient implementation of the transformation method of fuzzy arithmetic","authors":"A. Klimke","doi":"10.1109/NAFIPS.2003.1226830","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226830","url":null,"abstract":"The transformation method has been proposed for the simulation and analysis of systems with uncertain parameters. Here, several aspects of an efficient implementation are presented: fast processing of discretized fuzzy numbers through multi-dimensional arrays, elimination of recurring permutations, automatic decomposition of models, treatment of single occurrences of variables through interval arithmetic, and a monotonicity test based on automatic differentiation.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"293 2 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":"121264977","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.2003.1226750
J. M. Barone, P. Dewan
While studies of the role of fuzzy logic in natural language certainly exist, it is not clear that the use of fuzzy logic to represent linguistic constructs is anything more than an engineering convenience. This paper suggests that one reason this situation obtains is because fuzzy logic has been used strictly to elucidate static aspects of natural language (particularly aspects of the lexicon). If one examines dynamic features of natural language, on the other hand, new possibilities for connections between fuzzy logic and natural language emerge. In particular, some results from category theory are used to show that fuzzy logic can have a role in explaining certain otherwise rather obscure properties of linguistic comparatives in English.
{"title":"Looking for fuzziness in natural language","authors":"J. M. Barone, P. Dewan","doi":"10.1109/NAFIPS.2003.1226750","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226750","url":null,"abstract":"While studies of the role of fuzzy logic in natural language certainly exist, it is not clear that the use of fuzzy logic to represent linguistic constructs is anything more than an engineering convenience. This paper suggests that one reason this situation obtains is because fuzzy logic has been used strictly to elucidate static aspects of natural language (particularly aspects of the lexicon). If one examines dynamic features of natural language, on the other hand, new possibilities for connections between fuzzy logic and natural language emerge. In particular, some results from category theory are used to show that fuzzy logic can have a role in explaining certain otherwise rather obscure properties of linguistic comparatives in English.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"138 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":"127862322","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.2003.1226827
W.U. Syed
Computational models of the bargaining process at a farmers' market require modeling subjective preferences of the buyers and sellers and their subjective assessments of the produce. The proposed fuzzy agents based model employs a number of fuzzy inference systems modeled as Standard Additive Models that perform the subjective decision making for the agents. Survey results of different vendors and customers at different farmers' markets provide the rule base coded in these fuzzy expert systems. The results show a steady convergence to a bargain weighted towards the greedier of the two players. Repeated simulations with the proposed model of varying buyers, sellers and the produce indicate that fuzzy agents can model bargaining at a farmers' market.
{"title":"Fuzzy agents bargaining at a farmer's market","authors":"W.U. Syed","doi":"10.1109/NAFIPS.2003.1226827","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226827","url":null,"abstract":"Computational models of the bargaining process at a farmers' market require modeling subjective preferences of the buyers and sellers and their subjective assessments of the produce. The proposed fuzzy agents based model employs a number of fuzzy inference systems modeled as Standard Additive Models that perform the subjective decision making for the agents. Survey results of different vendors and customers at different farmers' markets provide the rule base coded in these fuzzy expert systems. The results show a steady convergence to a bargain weighted towards the greedier of the two players. Repeated simulations with the proposed model of varying buyers, sellers and the produce indicate that fuzzy agents can model bargaining at a farmers' market.","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":"126837616","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.2003.1226818
V. Kreinovich, Praveen Patangay, L. Longpré, S. Starks, Cynthia Campos
In many application areas, it is important to detect outliers. Traditional engineering approach to outlier detection is that we start with some "normal" values x/sub 1/,..., x/sub n/, compute the sample average E, the sample standard variation /spl sigma/, and then mark a value x as an outlier if x is outside the k/sub 0/-sigma interval [E-k/sub 0//spl middot//spl sigma/, E+k/sub 0//spl middot//spl sigma/] (for some pre-selected parameter k/sub 0/). In real life, we often have only interval ranges [x/sub i/, x~/sub i/] for the normal values x/sub 1/,...,x/sub n/. In this case, we only have intervals of possible values for the bounds E-k/sub 0//spl middot//spl sigma/ and E+k/sub 0//spl middot//spl sigma/. We can therefore identify outliers as values that are outside all k/sub 0/-sigma intervals. In this paper, we analyze the computational complexity of these outlier detection problems, and provide efficient algorithms that solve some of these problems (under reasonable conditions). We also provide algorithms that estimate the degree of "outlier-ness" of a given value x-measured as the largest value k/sub 0/ for which x is outside the corresponding k/sub 0/-sigma interval.
{"title":"Outlier detection under interval and fuzzy uncertainty: algorithmic solvability and computational complexity","authors":"V. Kreinovich, Praveen Patangay, L. Longpré, S. Starks, Cynthia Campos","doi":"10.1109/NAFIPS.2003.1226818","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226818","url":null,"abstract":"In many application areas, it is important to detect outliers. Traditional engineering approach to outlier detection is that we start with some \"normal\" values x/sub 1/,..., x/sub n/, compute the sample average E, the sample standard variation /spl sigma/, and then mark a value x as an outlier if x is outside the k/sub 0/-sigma interval [E-k/sub 0//spl middot//spl sigma/, E+k/sub 0//spl middot//spl sigma/] (for some pre-selected parameter k/sub 0/). In real life, we often have only interval ranges [x/sub i/, x~/sub i/] for the normal values x/sub 1/,...,x/sub n/. In this case, we only have intervals of possible values for the bounds E-k/sub 0//spl middot//spl sigma/ and E+k/sub 0//spl middot//spl sigma/. We can therefore identify outliers as values that are outside all k/sub 0/-sigma intervals. In this paper, we analyze the computational complexity of these outlier detection problems, and provide efficient algorithms that solve some of these problems (under reasonable conditions). We also provide algorithms that estimate the degree of \"outlier-ness\" of a given value x-measured as the largest value k/sub 0/ for which x is outside the corresponding k/sub 0/-sigma interval.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"86 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":"132853699","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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