Pub Date : 1996-12-11DOI: 10.1109/AFSS.1996.583645
Mei-Shiang Chang, H. Chen
The membership functions of fuzzy expert systems need a systematic, self-learning method instead of a subjective tuning method in order to increase the performance of the fuzzy model. The genetic-algorithm learning method is consequently employed. The rule-based encoding scheme would bring the redundant information for the genetic algorithm by repeatedly representing the similar membership function in an individual. The new encoding method, which is a parameter-based encoding scheme, would diminish the redundant representation of fuzzy parameters. This method would separate the data structures of fuzzy rules and fuzzy parameters in the genetic-algorithm learning method. This method should efficiently use the memory resources of computers and increase the dimensions of the solved problem. Then, a numerical example and the learning results are demonstrated. Discussions about the effects of population size, reproduction method, crossover rate, mutation rate and fitness scaling are included. Finally, some conclusions are presented.
{"title":"A new encoding method of genetic algorithms towards parameter identification of fuzzy expert systems","authors":"Mei-Shiang Chang, H. Chen","doi":"10.1109/AFSS.1996.583645","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583645","url":null,"abstract":"The membership functions of fuzzy expert systems need a systematic, self-learning method instead of a subjective tuning method in order to increase the performance of the fuzzy model. The genetic-algorithm learning method is consequently employed. The rule-based encoding scheme would bring the redundant information for the genetic algorithm by repeatedly representing the similar membership function in an individual. The new encoding method, which is a parameter-based encoding scheme, would diminish the redundant representation of fuzzy parameters. This method would separate the data structures of fuzzy rules and fuzzy parameters in the genetic-algorithm learning method. This method should efficiently use the memory resources of computers and increase the dimensions of the solved problem. Then, a numerical example and the learning results are demonstrated. Discussions about the effects of population size, reproduction method, crossover rate, mutation rate and fitness scaling are included. Finally, some conclusions are presented.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129201343","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 : 1996-12-11DOI: 10.1109/AFSS.1996.583542
A. Wasilewska
A concept of LT-fuzzy sets was introduced by Rasiowa and Cat Ho (1992). LT-fuzzy sets are a modification of L-fuzzy sets introduced by Goguen (1967). We introduce here a notion of a generalized rough set and show that it can be considered as a particular case of a L-fuzzy set. We also generalize the notion of a rough equality of sets, introduced by Pawlak in 1985 to a notion of topological equality of sets and we prove that the LT-fuzzy sets provide a common characterization for all of the considered concepts.
{"title":"On rough and LT-fuzzy sets","authors":"A. Wasilewska","doi":"10.1109/AFSS.1996.583542","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583542","url":null,"abstract":"A concept of LT-fuzzy sets was introduced by Rasiowa and Cat Ho (1992). LT-fuzzy sets are a modification of L-fuzzy sets introduced by Goguen (1967). We introduce here a notion of a generalized rough set and show that it can be considered as a particular case of a L-fuzzy set. We also generalize the notion of a rough equality of sets, introduced by Pawlak in 1985 to a notion of topological equality of sets and we prove that the LT-fuzzy sets provide a common characterization for all of the considered concepts.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116846080","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 : 1996-12-11DOI: 10.1109/AFSS.1996.583576
T. Murai, M. Nakata, M. Shimbo
A modal logical explanation is presented about how data conveyed by logical formulas generates relations, or tables, in databases as belief sets based on the idea of possible worlds restriction.
{"title":"Generation of relations as belief in databases","authors":"T. Murai, M. Nakata, M. Shimbo","doi":"10.1109/AFSS.1996.583576","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583576","url":null,"abstract":"A modal logical explanation is presented about how data conveyed by logical formulas generates relations, or tables, in databases as belief sets based on the idea of possible worlds restriction.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116930160","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 : 1996-12-11DOI: 10.1109/AFSS.1996.583683
T. Chu, Chung-Tsen Tsao, Yeou-Ren Shiue
The investor has to consider many factors when making a decision on which stocks to buy. However, judgements on these factors are usually linguistic, fuzzy, and conflicting. Therefore, selection of stocks is a fuzzy multiple attribute decision making (FMADM) problems. A hierarchical composite structure for factors and subfactors is developed for company analysis. A weight model is presented. Values of each subfactor are assumed to have normal distribution in order to build up the membership function of the ascending half-trapezoid. By multiplying the weight matrix with the corresponding fuzzy judgement matrix for each factor and calculating the weighted summation of weighted matrices, the authors make the fuzzy decision by grades. A numerical example of selecting the first priority stock among seven listed companies of the cement industry in Taiwan's stock market is applied to verify this model.
{"title":"Application of fuzzy multiple attribute decision making on company analysis for stock selection","authors":"T. Chu, Chung-Tsen Tsao, Yeou-Ren Shiue","doi":"10.1109/AFSS.1996.583683","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583683","url":null,"abstract":"The investor has to consider many factors when making a decision on which stocks to buy. However, judgements on these factors are usually linguistic, fuzzy, and conflicting. Therefore, selection of stocks is a fuzzy multiple attribute decision making (FMADM) problems. A hierarchical composite structure for factors and subfactors is developed for company analysis. A weight model is presented. Values of each subfactor are assumed to have normal distribution in order to build up the membership function of the ascending half-trapezoid. By multiplying the weight matrix with the corresponding fuzzy judgement matrix for each factor and calculating the weighted summation of weighted matrices, the authors make the fuzzy decision by grades. A numerical example of selecting the first priority stock among seven listed companies of the cement industry in Taiwan's stock market is applied to verify this model.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132806310","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 : 1996-12-11DOI: 10.1109/AFSS.1996.583599
Jyh-Ming Chen, Shuan-Hao Wu, Hahn-Ming Lee
This research is based on a fuzzy neural network, named knowledge-based neural network with trapezoid fuzzy set inputs (KBNN/TFS). We use this network model to refine fuzzy rules with a training database. We propose an interactive consistency checking engine with automatic rule insertion and deletion (ICE/RID) to perform fuzzy rule verification. This process is used to verify the initial rule base and the rules refined by KBNN/TFS. With the interactive interface of ICE, we can detect redundant rules, subsumed rules, and conflict rules. Besides, we can also use RID to insert and delete fuzzy rules automatically if necessary. The proposed model is tested with an inverted pendulum system (IPS). In these experiments, we demonstrate the ability of ICE/RID to remove inconsistencies and insert rules in KBNN/TFS. With the combination of ICE/RID and KBNN/TFS, a valid and consistent rule base can be obtained.
{"title":"The study of automatic insertion and deletion of fuzzy rules in fuzzy neural network models","authors":"Jyh-Ming Chen, Shuan-Hao Wu, Hahn-Ming Lee","doi":"10.1109/AFSS.1996.583599","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583599","url":null,"abstract":"This research is based on a fuzzy neural network, named knowledge-based neural network with trapezoid fuzzy set inputs (KBNN/TFS). We use this network model to refine fuzzy rules with a training database. We propose an interactive consistency checking engine with automatic rule insertion and deletion (ICE/RID) to perform fuzzy rule verification. This process is used to verify the initial rule base and the rules refined by KBNN/TFS. With the interactive interface of ICE, we can detect redundant rules, subsumed rules, and conflict rules. Besides, we can also use RID to insert and delete fuzzy rules automatically if necessary. The proposed model is tested with an inverted pendulum system (IPS). In these experiments, we demonstrate the ability of ICE/RID to remove inconsistencies and insert rules in KBNN/TFS. With the combination of ICE/RID and KBNN/TFS, a valid and consistent rule base can be obtained.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128996563","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 : 1996-12-11DOI: 10.1109/AFSS.1996.583601
M. Nakata
When possible values are considered in update operations, unacceptable possible values are created. There are three kinds of unacceptable possible values. The unacceptable possible values can be eliminated from relations without loss of information. By considering this point, update operations can be executed without paying attention to unacceptable possible values.
{"title":"Update operations considering possible values in fuzzy databases","authors":"M. Nakata","doi":"10.1109/AFSS.1996.583601","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583601","url":null,"abstract":"When possible values are considered in update operations, unacceptable possible values are created. There are three kinds of unacceptable possible values. The unacceptable possible values can be eliminated from relations without loss of information. By considering this point, update operations can be executed without paying attention to unacceptable possible values.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129033641","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 : 1996-12-11DOI: 10.1109/AFSS.1996.583556
Z. Yeh, Hung-Pin Chen
This paper presents a methodology for the design of a binary tree multi-stage inference fuzzy controller in which the consequence in an inference stage is passed to the next stage as fact, and so forth. A new general method which is based on a performance index of the control system is used to generate fuzzy rule bases for bi-tree multi-stage inference. This proposed method can be used to reduce the complexity of fuzzy rule sets. The new method has been applied to control a truck-and-two-trailer system. The simulation studies showed that the proposed method is feasible.
{"title":"A bi-tree multi-stage inference fuzzy control system","authors":"Z. Yeh, Hung-Pin Chen","doi":"10.1109/AFSS.1996.583556","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583556","url":null,"abstract":"This paper presents a methodology for the design of a binary tree multi-stage inference fuzzy controller in which the consequence in an inference stage is passed to the next stage as fact, and so forth. A new general method which is based on a performance index of the control system is used to generate fuzzy rule bases for bi-tree multi-stage inference. This proposed method can be used to reduce the complexity of fuzzy rule sets. The new method has been applied to control a truck-and-two-trailer system. The simulation studies showed that the proposed method is feasible.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115647440","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 : 1996-12-11DOI: 10.1109/AFSS.1996.583615
C. Fu, H. Wang
This study proposes a fuzzy resource allocation model for project management in which a fuzzy relation on resource needs and a budget limit are described. The model can be solved in the form of crisp linear programming (LP) with /spl alpha/-cut. Despite the delivery routes of the teams, the results can express whether the project has sufficient or insufficient resources resulting from each activity.
{"title":"Fuzzy resource allocations in project management when insufficient resources are considered","authors":"C. Fu, H. Wang","doi":"10.1109/AFSS.1996.583615","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583615","url":null,"abstract":"This study proposes a fuzzy resource allocation model for project management in which a fuzzy relation on resource needs and a budget limit are described. The model can be solved in the form of crisp linear programming (LP) with /spl alpha/-cut. Despite the delivery routes of the teams, the results can express whether the project has sufficient or insufficient resources resulting from each activity.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115772302","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 : 1996-12-11DOI: 10.1109/AFSS.1996.583580
S. Tsumoto, H. Tanaka
A rule-induction system, called PRIMEROSE3 (probabilistic rule induction method based on rough sets version 3.0), is introduced. This program first analyzes the statistical characteristics of attribute-value pairs from training samples, then determines what kind of diagnosing model can be applied to the training samples. Then, it extracts not only classification rules for differential diagnosis, but also other medical knowledge needed for other diagnostic procedures in a selected diagnosing model. PRIMEROSE3 is evaluated on three kinds of clinical databases and the induced results are compared with domain knowledge acquired from medical experts, including classification rules. The experimental results show that our proposed method correctly not only selects a diagnosing model, but also extracts domain knowledge.
{"title":"Extraction of diagnostic knowledge from clinical databases based on rough set theory","authors":"S. Tsumoto, H. Tanaka","doi":"10.1109/AFSS.1996.583580","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583580","url":null,"abstract":"A rule-induction system, called PRIMEROSE3 (probabilistic rule induction method based on rough sets version 3.0), is introduced. This program first analyzes the statistical characteristics of attribute-value pairs from training samples, then determines what kind of diagnosing model can be applied to the training samples. Then, it extracts not only classification rules for differential diagnosis, but also other medical knowledge needed for other diagnostic procedures in a selected diagnosing model. PRIMEROSE3 is evaluated on three kinds of clinical databases and the induced results are compared with domain knowledge acquired from medical experts, including classification rules. The experimental results show that our proposed method correctly not only selects a diagnosing model, but also extracts domain knowledge.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125328781","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 : 1996-12-11DOI: 10.1109/AFSS.1996.583596
Ching-Wen Ma, C. Teng
The single-source near-field direction finding problem can be solved by a fuzzy neural network (FNN). The FNN approach can be applied to arrays with arbitrary configurations. It can also be implemented for real-time tracking applications. The approach outperforms the far-field approximation (FFA) approach when the array is uniformly-spaced and linear, especially when the angle between the array normal direction and the source direction is large and the distance from array center to the source is short.
{"title":"Near-field direction finding with a fuzzy neural network","authors":"Ching-Wen Ma, C. Teng","doi":"10.1109/AFSS.1996.583596","DOIUrl":"https://doi.org/10.1109/AFSS.1996.583596","url":null,"abstract":"The single-source near-field direction finding problem can be solved by a fuzzy neural network (FNN). The FNN approach can be applied to arrays with arbitrary configurations. It can also be implemented for real-time tracking applications. The approach outperforms the far-field approximation (FFA) approach when the array is uniformly-spaced and linear, especially when the angle between the array normal direction and the source direction is large and the distance from array center to the source is short.","PeriodicalId":197019,"journal":{"name":"Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124512067","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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