Pub Date : 2013-08-12DOI: 10.1142/S0218488513400072
Jinwu Gao, Xiangfeng Yang
In credibilistic bimatrix games, the solution concept of (α, β)-optimistic equilibrium strategy was proposed for dealing with the situation that the two players want to optimize the optimistic value of their fuzzy objectives at confidence levels α and β, respectively. This paper goes further by assuming that the confidence levels are private information of the two players. And the so-called credibilistic bimatrix game with asymmetric information is investigated. A solution concept of Bayesian optimistic equilibrium strategy as well as its existence theorem are presented. Moreover, a sufficient and necessary condition is given for finding the Bayesian optimistic equilibrium strategy. Finally, an example is provided for illustrating purpose.
{"title":"CREDIBILISTIC BIMATRIX GAME WITH ASYMMETRIC INFORMATION: BAYESIAN OPTIMISTIC EQUILIBRIUM STRATEGY","authors":"Jinwu Gao, Xiangfeng Yang","doi":"10.1142/S0218488513400072","DOIUrl":"https://doi.org/10.1142/S0218488513400072","url":null,"abstract":"In credibilistic bimatrix games, the solution concept of (α, β)-optimistic equilibrium strategy was proposed for dealing with the situation that the two players want to optimize the optimistic value of their fuzzy objectives at confidence levels α and β, respectively. This paper goes further by assuming that the confidence levels are private information of the two players. And the so-called credibilistic bimatrix game with asymmetric information is investigated. A solution concept of Bayesian optimistic equilibrium strategy as well as its existence theorem are presented. Moreover, a sufficient and necessary condition is given for finding the Bayesian optimistic equilibrium strategy. Finally, an example is provided for illustrating purpose.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"13 1","pages":"89-100"},"PeriodicalIF":1.5,"publicationDate":"2013-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90794632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-12DOI: 10.1142/S0218488513400047
LuhandjulaMonga Kalonda, Rangoaga Moeti Joseph
In this paper we propose an approach for multiobjective programming problems with fuzzy number coefficients. The main idea behind our approach is to approximate involved fuzzy numbers by their respective nearest interval approximation counterparts. An algorithm that returns a nearest interval approximation to a given fuzzy number, plays a pivotal role in the proposed method. Our approach contrasts markedly with those based on deffuzification operators which replace a fuzzy set by a single real number leading to a loss of many important information. A numerical example is also provided for the sake of illustration.
{"title":"MATHEMATICAL PROGRAMMING PROBLEMS WITH SEVERAL FUZZY OBJECTIVE FUNCTIONS","authors":"LuhandjulaMonga Kalonda, Rangoaga Moeti Joseph","doi":"10.1142/S0218488513400047","DOIUrl":"https://doi.org/10.1142/S0218488513400047","url":null,"abstract":"In this paper we propose an approach for multiobjective programming problems with fuzzy number coefficients. The main idea behind our approach is to approximate involved fuzzy numbers by their respective nearest interval approximation counterparts. An algorithm that returns a nearest interval approximation to a given fuzzy number, plays a pivotal role in the proposed method. Our approach contrasts markedly with those based on deffuzification operators which replace a fuzzy set by a single real number leading to a loss of many important information. A numerical example is also provided for the sake of illustration.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"5 1","pages":"51-62"},"PeriodicalIF":1.5,"publicationDate":"2013-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90099939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-12DOI: 10.1142/S0218488513400060
Linxue Sheng, Yuanguo Zhu
Optimal control is an important field of study both in theory and in applications. Based on uncertainty theory, an expected value model of uncertain optimal control problem was studied by Zhu. In this paper, an optimistic value model for uncertain optimal control problem is investigated. Applying Bellman's principle of optimality, the principle of optimality for the model is presented. And then the equation of optimality is obtained for the optimistic value model of uncertain optimal control. Finally, a portfolio selection problem is solved by this equation of optimality.
{"title":"OPTIMISTIC VALUE MODEL OF UNCERTAIN OPTIMAL CONTROL","authors":"Linxue Sheng, Yuanguo Zhu","doi":"10.1142/S0218488513400060","DOIUrl":"https://doi.org/10.1142/S0218488513400060","url":null,"abstract":"Optimal control is an important field of study both in theory and in applications. Based on uncertainty theory, an expected value model of uncertain optimal control problem was studied by Zhu. In this paper, an optimistic value model for uncertain optimal control problem is investigated. Applying Bellman's principle of optimality, the principle of optimality for the model is presented. And then the equation of optimality is obtained for the optimistic value model of uncertain optimal control. Finally, a portfolio selection problem is solved by this equation of optimality.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"17 1","pages":"75-87"},"PeriodicalIF":1.5,"publicationDate":"2013-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77985610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-12DOI: 10.1142/S0218488513400011
Xiang Li, Xingxing Yang
With fixed running times at sections, cooperative scheduling (CS) approach optimizes the dwell times and the headway time to coordinate the accelerating and braking processes for trains, such that the recovery energy generated from the braking trains can be used by the accelerating trains. In practice, trains always have stochastic departure delays at busy stations. For reducing the divergence from the given timetable, the operation company generally adjusts the running times at the following sections. Focusing on the randomness on delay times and running times, this paper proposes a stochastic cooperative scheduling (SCS) approach. Firstly, we estimate the conversion and transmission losses of recovery energy, and then formulate a stochastic expected value model to maximize the utilization of the recovery energy. Furthermore, we design a binary-coded genetic algorithm to solve the optimal timetable. Finally, we conduct experimental studies based on the operation data from Beijing Yizhuang subway line. The results show that the SCS approach can save energy by 15.13% compared with the current timetable, and 8.81% compared with the CS approach.
{"title":"A STOCHASTIC TIMETABLE OPTIMIZATION MODEL IN SUBWAY SYSTEMS","authors":"Xiang Li, Xingxing Yang","doi":"10.1142/S0218488513400011","DOIUrl":"https://doi.org/10.1142/S0218488513400011","url":null,"abstract":"With fixed running times at sections, cooperative scheduling (CS) approach optimizes the dwell times and the headway time to coordinate the accelerating and braking processes for trains, such that the recovery energy generated from the braking trains can be used by the accelerating trains. In practice, trains always have stochastic departure delays at busy stations. For reducing the divergence from the given timetable, the operation company generally adjusts the running times at the following sections. Focusing on the randomness on delay times and running times, this paper proposes a stochastic cooperative scheduling (SCS) approach. Firstly, we estimate the conversion and transmission losses of recovery energy, and then formulate a stochastic expected value model to maximize the utilization of the recovery energy. Furthermore, we design a binary-coded genetic algorithm to solve the optimal timetable. Finally, we conduct experimental studies based on the operation data from Beijing Yizhuang subway line. The results show that the SCS approach can save energy by 15.13% compared with the current timetable, and 8.81% compared with the CS approach.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"95 1","pages":"1-15"},"PeriodicalIF":1.5,"publicationDate":"2013-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90983315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-11DOI: 10.1142/S021848851240020X
Chun-Hao Chen, T. Hong, Yeong-Chyi Lee
Data mining is most commonly used in attempts to induce association rules from transaction data. Since transactions in real-world applications usually consist of quantitative values, many fuzzy association-rule mining approaches have been proposed on single- or multiple-concept levels. However, the given membership functions may have a critical influence on the final mining results. In this paper, we propose a multiple-level genetic-fuzzy mining algorithm for mining membership functions and fuzzy association rules using multiple-concept levels. It first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. The fitness value of each individual is then evaluated by the summation of large 1-itemsets of each item in different concept levels and the suitability of membership functions in the chromosome. After the GA process terminates, a better set of multiple-level fuzzy association rules can then be expected with a more suitable set of membership functions. Experimental results on a simulation dataset also show the effectiveness of the algorithm.
{"title":"GENETIC-FUZZY MINING WITH TAXONOMY","authors":"Chun-Hao Chen, T. Hong, Yeong-Chyi Lee","doi":"10.1142/S021848851240020X","DOIUrl":"https://doi.org/10.1142/S021848851240020X","url":null,"abstract":"Data mining is most commonly used in attempts to induce association rules from transaction data. Since transactions in real-world applications usually consist of quantitative values, many fuzzy association-rule mining approaches have been proposed on single- or multiple-concept levels. However, the given membership functions may have a critical influence on the final mining results. In this paper, we propose a multiple-level genetic-fuzzy mining algorithm for mining membership functions and fuzzy association rules using multiple-concept levels. It first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. The fitness value of each individual is then evaluated by the summation of large 1-itemsets of each item in different concept levels and the suitability of membership functions in the chromosome. After the GA process terminates, a better set of multiple-level fuzzy association rules can then be expected with a more suitable set of membership functions. Experimental results on a simulation dataset also show the effectiveness of the algorithm.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"22 1","pages":"187-205"},"PeriodicalIF":1.5,"publicationDate":"2012-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73493160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-11DOI: 10.1142/S0218488512400211
Daoyuan Zhai, M. Hao, J. Mendel
Removing Mixed Gaussian and Impulse Noise (MGIN) is considered to be one of the most essential topics in the domain of image restoration, and it is much more challenging than to remove pure Gaussian or impulse noise separately. Therefore, relatively fewer works have been published in this area. This paper proposes a new integrated approach for MGIN removal that is based on a Non-Singleton Interval Type-2 (NS-IT2) Fuzzy Logic System (FLS), and explains how to design such a NS-IT2 FLS using a Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. Then the paper goes on to introduce two supplementary components, a Block-Matching 3-Dimensional Discrete Cosine Transformation (BM3D DCT) filter and a contrast scaling filter, which augment the overall performance of the NS-IT2 FLS. Finally, the paper shows that this proposed approach indeed provides both quantitatively and visually much better results compared to other often-used non-fuzzy techniques as well as its Type-1 (T1) and singleton IT2 (S-IT2) counterparts.
{"title":"UNIVERSAL IMAGE NOISE REMOVAL FILTER BASED ON TYPE-2 FUZZY LOGIC SYSTEM AND QPSO","authors":"Daoyuan Zhai, M. Hao, J. Mendel","doi":"10.1142/S0218488512400211","DOIUrl":"https://doi.org/10.1142/S0218488512400211","url":null,"abstract":"Removing Mixed Gaussian and Impulse Noise (MGIN) is considered to be one of the most essential topics in the domain of image restoration, and it is much more challenging than to remove pure Gaussian or impulse noise separately. Therefore, relatively fewer works have been published in this area. This paper proposes a new integrated approach for MGIN removal that is based on a Non-Singleton Interval Type-2 (NS-IT2) Fuzzy Logic System (FLS), and explains how to design such a NS-IT2 FLS using a Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. Then the paper goes on to introduce two supplementary components, a Block-Matching 3-Dimensional Discrete Cosine Transformation (BM3D DCT) filter and a contrast scaling filter, which augment the overall performance of the NS-IT2 FLS. Finally, the paper shows that this proposed approach indeed provides both quantitatively and visually much better results compared to other often-used non-fuzzy techniques as well as its Type-1 (T1) and singleton IT2 (S-IT2) counterparts.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"11 1","pages":"207-232"},"PeriodicalIF":1.5,"publicationDate":"2012-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82383495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-11DOI: 10.1142/S0218488512400223
S. Fukuda, B. Baets
Information on species distributions is of key importance when designing management plans for a target species or ecosystem. This paper illustrates the effects of absence data on fish habitat prediction and habitat preference evaluation using a genetic Takagi-Sugeno fuzzy model. Three independent data sets were prepared from a series of fish habitat surveys conducted in an agricultural canal in Japan. To quantify the effects of absence data, two kinds of abundance data (entire data and presence data) were used for developing a fuzzy habitat preference model (FHPM). As a result, habitat preference curves (HPCs) obtained from presence data resulted in similar HPCs between the three data sets, while those obtained from entire data slightly differed according to the data sets. The higher generalization ability of the FHPMs obtained from presence data supports the usefulness of presence data for better extracting the habitat preference information of a target species from field observation data.
{"title":"DO ABSENCE DATA MATTER WHEN MODELLING FISH HABITAT PREFERENCE USING A GENETIC TAKAGI-SUGENO FUZZY MODEL?","authors":"S. Fukuda, B. Baets","doi":"10.1142/S0218488512400223","DOIUrl":"https://doi.org/10.1142/S0218488512400223","url":null,"abstract":"Information on species distributions is of key importance when designing management plans for a target species or ecosystem. This paper illustrates the effects of absence data on fish habitat prediction and habitat preference evaluation using a genetic Takagi-Sugeno fuzzy model. Three independent data sets were prepared from a series of fish habitat surveys conducted in an agricultural canal in Japan. To quantify the effects of absence data, two kinds of abundance data (entire data and presence data) were used for developing a fuzzy habitat preference model (FHPM). As a result, habitat preference curves (HPCs) obtained from presence data resulted in similar HPCs between the three data sets, while those obtained from entire data slightly differed according to the data sets. The higher generalization ability of the FHPMs obtained from presence data supports the usefulness of presence data for better extracting the habitat preference information of a target species from field observation data.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"34 1","pages":"233-245"},"PeriodicalIF":1.5,"publicationDate":"2012-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78743434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-11DOI: 10.1142/S0218488512400168
D. Stavrakoudis, J. Theocharis
Many modern classification tasks are defined in highly-dimensional feature spaces. The derivation of high-performing genetic fuzzy rule-based classification systems (GFRBCSs) in such scenarios is a non-trivial task. This paper presents a framework for increasing the performance of GFRBCSs by creating a hierarchical fuzzy rule-based classifier. The proposed system is constructed through repeated invocations to a base GFRBCS procedure, considering at each step an input space fuzzy partition of a certain granularity. The best performing rules are inserted in the hierarchical rule base and the process is repeated again, considering a thicker granularity. The employed boosting scheme guides the algorithm in creating new rules to treat uncovered or misclassified patterns, thus monotonically increasing the performance of the classifier. Extensive experimental analysis in a number of real-world high-dimensional classification tasks proves the effectiveness of the proposed approach in increasing the performance of the base classifier, maintaining its interpretability to a considerable degree.
{"title":"HANDLING HIGHLY-DIMENSIONAL CLASSIFICATION TASKS WITH HIERARCHICAL GENETIC FUZZY RULE-BASED CLASSIFIERS","authors":"D. Stavrakoudis, J. Theocharis","doi":"10.1142/S0218488512400168","DOIUrl":"https://doi.org/10.1142/S0218488512400168","url":null,"abstract":"Many modern classification tasks are defined in highly-dimensional feature spaces. The derivation of high-performing genetic fuzzy rule-based classification systems (GFRBCSs) in such scenarios is a non-trivial task. This paper presents a framework for increasing the performance of GFRBCSs by creating a hierarchical fuzzy rule-based classifier. The proposed system is constructed through repeated invocations to a base GFRBCS procedure, considering at each step an input space fuzzy partition of a certain granularity. The best performing rules are inserted in the hierarchical rule base and the process is repeated again, considering a thicker granularity. The employed boosting scheme guides the algorithm in creating new rules to treat uncovered or misclassified patterns, thus monotonically increasing the performance of the classifier. Extensive experimental analysis in a number of real-world high-dimensional classification tasks proves the effectiveness of the proposed approach in increasing the performance of the base classifier, maintaining its interpretability to a considerable degree.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"34 1","pages":"73-104"},"PeriodicalIF":1.5,"publicationDate":"2012-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84419518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-11DOI: 10.1142/S0218488512400156
Ana M. Palacios, L. Sánchez, Inés Couso
An extension of the Adaboost algorithm for obtaining fuzzy rule-based systems from low quality data is combined with preprocessing algorithms for equalizing imbalanced datasets. With the help of synthetic and real-world problems, it is shown that the performance of the Adaboost algorithm is degraded in presence of a moderate uncertainty in either the input or the output values. It is also established that a preprocessing stage improves the accuracy of the classifier in a wide range of binary classification problems, including those whose imbalance ratio is uncertain.
{"title":"COMBINING ADABOOST WITH PREPROCESSING ALGORITHMS FOR EXTRACTING FUZZY RULES FROM LOW QUALITY DATA IN POSSIBLY IMBALANCED PROBLEMS","authors":"Ana M. Palacios, L. Sánchez, Inés Couso","doi":"10.1142/S0218488512400156","DOIUrl":"https://doi.org/10.1142/S0218488512400156","url":null,"abstract":"An extension of the Adaboost algorithm for obtaining fuzzy rule-based systems from low quality data is combined with preprocessing algorithms for equalizing imbalanced datasets. With the help of synthetic and real-world problems, it is shown that the performance of the Adaboost algorithm is degraded in presence of a moderate uncertainty in either the input or the output values. It is also established that a preprocessing stage improves the accuracy of the classifier in a wide range of binary classification problems, including those whose imbalance ratio is uncertain.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"1 1","pages":"51-71"},"PeriodicalIF":1.5,"publicationDate":"2012-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83039698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-09-11DOI: 10.1142/S0218488512400235
Chang-Shing Lee, Mei-Hui Wang, H. Hagras, Zhi-Wei Chen, Shun-Teng Lan, Chin-Yuan Hsu, S. Kuo, H. Kuo, Hui-Hua Cheng
In this paper, we present a novel Genetic Fuzzy Markup Language (GFML)-based genetic fuzzy system, including the genetic learning base, the knowledge base and rule base of FML, the fuzzy inference engine, and the genetic learning mechanism. The GFML is applied to the genetic fuzzy system for dealing with the knowledge base, the rule base, and the genetic learning base of the healthy diet domain, including the ingredients and the contained servings of six food categories of some common food in Taiwan. Moreover, the proposed novel system is able to infer the healthy status of human's daily eating. In the proposed system, the domain experts first define the nutrient facts of the common food to construct the fuzzy food ontology. Meanwhile, the involved Taiwanese students of National University of Tainan (NUTN) record their daily meals for a constant period of time. Then, based on the built fuzzy profile ontology, fuzzy food ontology, and fuzzy personal food ontology, a GFML-based genetic fuzzy system is carried out to infer the possibility of dietary healthy level for one-day meals. The experimental results show that the proposed GFML-based genetic fuzzy system gives good results for the healthy diet assessment.
{"title":"A NOVEL GENETIC FUZZY MARKUP LANGUAGE AND ITS APPLICATION TO HEALTHY DIET ASSESSMENT","authors":"Chang-Shing Lee, Mei-Hui Wang, H. Hagras, Zhi-Wei Chen, Shun-Teng Lan, Chin-Yuan Hsu, S. Kuo, H. Kuo, Hui-Hua Cheng","doi":"10.1142/S0218488512400235","DOIUrl":"https://doi.org/10.1142/S0218488512400235","url":null,"abstract":"In this paper, we present a novel Genetic Fuzzy Markup Language (GFML)-based genetic fuzzy system, including the genetic learning base, the knowledge base and rule base of FML, the fuzzy inference engine, and the genetic learning mechanism. The GFML is applied to the genetic fuzzy system for dealing with the knowledge base, the rule base, and the genetic learning base of the healthy diet domain, including the ingredients and the contained servings of six food categories of some common food in Taiwan. Moreover, the proposed novel system is able to infer the healthy status of human's daily eating. In the proposed system, the domain experts first define the nutrient facts of the common food to construct the fuzzy food ontology. Meanwhile, the involved Taiwanese students of National University of Tainan (NUTN) record their daily meals for a constant period of time. Then, based on the built fuzzy profile ontology, fuzzy food ontology, and fuzzy personal food ontology, a GFML-based genetic fuzzy system is carried out to infer the possibility of dietary healthy level for one-day meals. The experimental results show that the proposed GFML-based genetic fuzzy system gives good results for the healthy diet assessment.","PeriodicalId":50283,"journal":{"name":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","volume":"29 1","pages":"247-278"},"PeriodicalIF":1.5,"publicationDate":"2012-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74000090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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