Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295426
Chen-Chia Chuang, Chia-Chu Hsu, Jin-Tsong Jeng
In this paper, we integrate the techniques of cerebellar model articulation controller with general basis function (CMAC-GBF) and support vector regression (SVR) to develop a more efficient scheme. The advantages of CMAC-GBF include: fast learning speed, guarantee learning convergence, capability of derivative, etc. On the other hand, a SVR is a novel method for tackling the problems of function approximation and regression estimation based on the statistical learning theory and has robust properties that against noise. In this paper, we propose the SVR-based CMAC-GBF systems that combined SVR with CMAC-GBF systems. From the results of simulation, the proposed structure has high accuracy and noise against. Besides, the experimental testing results demonstrate that the SVR-based CMAC-GBF systems outperform the original CMAC-GBF systems.
{"title":"Integration of CMAC-GBF and Support Vector Regression Techniques","authors":"Chen-Chia Chuang, Chia-Chu Hsu, Jin-Tsong Jeng","doi":"10.1109/FUZZY.2007.4295426","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295426","url":null,"abstract":"In this paper, we integrate the techniques of cerebellar model articulation controller with general basis function (CMAC-GBF) and support vector regression (SVR) to develop a more efficient scheme. The advantages of CMAC-GBF include: fast learning speed, guarantee learning convergence, capability of derivative, etc. On the other hand, a SVR is a novel method for tackling the problems of function approximation and regression estimation based on the statistical learning theory and has robust properties that against noise. In this paper, we propose the SVR-based CMAC-GBF systems that combined SVR with CMAC-GBF systems. From the results of simulation, the proposed structure has high accuracy and noise against. Besides, the experimental testing results demonstrate that the SVR-based CMAC-GBF systems outperform the original CMAC-GBF systems.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125640405","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295365
R. Precup, Z. Preitl, S. Preitl
The paper proposes an original development method of Pi-fuzzy controllers. Accepting the controlled plants being described with simplified linear mathematical models, the method starts with linear PI controller development in terms of Iterative Feedback Tuning expressed in discrete-time. It is accompanied by a linear development method applied in delta domain. Next, the results of the linear development are transferred to the development of the fuzzy blocks in PI-fuzzy controllers by the modal equivalence principle, resulting in new two-degree-of-freedom Mamdani Pi-fuzzy controllers. Realtime experimental results in controlling a nonlinear servo-system validate the development method, where the linear development is performed using the Extended Symmetrical Optimum method.
本文提出了一种新颖的pi -模糊控制器开发方法。该方法接受被控对象用简化的线性数学模型来描述，从线性PI控制器的开发开始，用离散时间表示迭代反馈整定。并在delta域上应用了线性展开法。然后，利用模态等效原理将线性展开的结果转移到pi -模糊控制器中模糊块的展开中，从而得到新的二自由度Mamdani pi -模糊控制器。控制非线性伺服系统的实时实验结果验证了该开发方法，其中采用扩展对称优化方法进行线性开发。
{"title":"Iterative Feedback Tuning Approach to Development of PI-Fuzzy Controllers","authors":"R. Precup, Z. Preitl, S. Preitl","doi":"10.1109/FUZZY.2007.4295365","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295365","url":null,"abstract":"The paper proposes an original development method of Pi-fuzzy controllers. Accepting the controlled plants being described with simplified linear mathematical models, the method starts with linear PI controller development in terms of Iterative Feedback Tuning expressed in discrete-time. It is accompanied by a linear development method applied in delta domain. Next, the results of the linear development are transferred to the development of the fuzzy blocks in PI-fuzzy controllers by the modal equivalence principle, resulting in new two-degree-of-freedom Mamdani Pi-fuzzy controllers. Realtime experimental results in controlling a nonlinear servo-system validate the development method, where the linear development is performed using the Extended Symmetrical Optimum method.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122721535","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295514
J. Jacas, J. Recasens
Lipschitzian and kernel aggregation operators with respect to the natural T-indistinguishability operator Et and their powers are studied. A t-norm T is proved to be E T -Lipschitzian, and is interpreted as a fuzzy point and a fuzzy map as well. Given an Archimedean t-norm T with additive generator t, the quasi-arithmetic mean generated by t is proved to be the most stable aggregation operator with respect to T.
{"title":"Aggregation Operators and the Lipschitzian Condition","authors":"J. Jacas, J. Recasens","doi":"10.1109/FUZZY.2007.4295514","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295514","url":null,"abstract":"Lipschitzian and kernel aggregation operators with respect to the natural T-indistinguishability operator Et and their powers are studied. A t-norm T is proved to be E T -Lipschitzian, and is interpreted as a fuzzy point and a fuzzy map as well. Given an Archimedean t-norm T with additive generator t, the quasi-arithmetic mean generated by t is proved to be the most stable aggregation operator with respect to T.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131511980","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295516
E. Schmitt, V. Bombardier, P. Charpentier
This article presents a self-fuzzification method to enhance the settings of a fuzzy reasoning classification adapted to the automated inspection of wooden boards. The supervised classification is made thanks to fuzzy linguistic rules generated from small training data sets. This study especially answers to a double industrial need about the pattern recognition in wooden boards. Firstly, few samples are available to generate the recognition model. This aspect makes lesser efficient compilation methods like neural networks in terms of recognition rates. Secondly, the settings of the classification method must be simplified, because the users are not experts in fuzzy logic. In this article, two points are presented. The first part demonstrates the generalization capability of the presented classification method in comparison to more classical algorithms. In the second part, we propose a new automatic method of parameter fuzzification, by using the typicality correlation coefficients of each class.
{"title":"Self-Fuzzification Method according to Typicality Correlation for Classification on tiny Data Sets","authors":"E. Schmitt, V. Bombardier, P. Charpentier","doi":"10.1109/FUZZY.2007.4295516","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295516","url":null,"abstract":"This article presents a self-fuzzification method to enhance the settings of a fuzzy reasoning classification adapted to the automated inspection of wooden boards. The supervised classification is made thanks to fuzzy linguistic rules generated from small training data sets. This study especially answers to a double industrial need about the pattern recognition in wooden boards. Firstly, few samples are available to generate the recognition model. This aspect makes lesser efficient compilation methods like neural networks in terms of recognition rates. Secondly, the settings of the classification method must be simplified, because the users are not experts in fuzzy logic. In this article, two points are presented. The first part demonstrates the generalization capability of the presented classification method in comparison to more classical algorithms. In the second part, we propose a new automatic method of parameter fuzzification, by using the typicality correlation coefficients of each class.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128148642","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295527
A. Botta, B. Lazzerini, F. Marcelloni, D. Stefanescu
In the framework of context adaptation of fuzzy systems, a typical requirement of a contextualized system is to maintain the same interpretability as the original one. Here, we propose a novel index based on a fuzzy ordering relation to provide a measure of interpretability. Our index assesses ordering, distinguishability and coverage at the same time. We use the proposed index and the mean square error as goals of a multi-objective genetic algorithm aimed at generating contextualized Mamdani fuzzy systems with different trade-offs between the two goals. Results obtained on a synthetic data set are also discussed.
{"title":"Exploiting Fuzzy Ordering Relations to Preserve Interpretability in Context Adaptation of Fuzzy Systems","authors":"A. Botta, B. Lazzerini, F. Marcelloni, D. Stefanescu","doi":"10.1109/FUZZY.2007.4295527","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295527","url":null,"abstract":"In the framework of context adaptation of fuzzy systems, a typical requirement of a contextualized system is to maintain the same interpretability as the original one. Here, we propose a novel index based on a fuzzy ordering relation to provide a measure of interpretability. Our index assesses ordering, distinguishability and coverage at the same time. We use the proposed index and the mean square error as goals of a multi-objective genetic algorithm aimed at generating contextualized Mamdani fuzzy systems with different trade-offs between the two goals. Results obtained on a synthetic data set are also discussed.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133353429","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295401
R. Sepúlveda, O. Castillo, P. Melin, O. Montiel, L. Aguilar
Uncertainty is an inherent part in controllers used for real-world applications. The use of new methods for handling incomplete information is of fundamental importance in engineering applications. We simulated the effects of uncertainty produced by the instrumentation elements in type-1 and type-2 fuzzy logic controllers to perform a comparative analysis of the systems' response, in the presence of uncertainty. We are presenting an innovative idea to optimize interval type-2 membership functions using an average of two type-1 systems with the Human Evolutionary Model, and we show comparative results of the optimized proposed method. We found that the optimized membership functions for the inputs of a type-2 system increases the performance of the system for high noise levels.
{"title":"Evolutionary optimization of interval type-2 membership functions using the Human Evolutionary Model","authors":"R. Sepúlveda, O. Castillo, P. Melin, O. Montiel, L. Aguilar","doi":"10.1109/FUZZY.2007.4295401","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295401","url":null,"abstract":"Uncertainty is an inherent part in controllers used for real-world applications. The use of new methods for handling incomplete information is of fundamental importance in engineering applications. We simulated the effects of uncertainty produced by the instrumentation elements in type-1 and type-2 fuzzy logic controllers to perform a comparative analysis of the systems' response, in the presence of uncertainty. We are presenting an innovative idea to optimize interval type-2 membership functions using an average of two type-1 systems with the Human Evolutionary Model, and we show comparative results of the optimized proposed method. We found that the optimized membership functions for the inputs of a type-2 system increases the performance of the system for high noise levels.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131767121","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295381
A. Kasperski, P. Zieliński
This paper deals with a general combinatorial optimization problem with fuzzy costs. The set of nondominated solutions with respect to an assumed fuzzy preference relation, according to the Orlovski's concept, is supposed to be the solution of the problem. A special attention is paid to the unfuzzy nondominated solutions (the solutions which are nondominated to the degree one). The main results of the paper are several new, weakened conditions on a fuzzy preference relation that allow to reduce the problem of determining unfuzzy nondominated solutions to the underling problem with deterministic costs. These solutions can be obtained by means of classical algorithms for the underling crisp problem, avoiding a construction of the special ones for the fuzzy problem. Moreover, it is shown that several known from literature fuzzy preference relations fulfill the proposed conditions. The approach is illustrated by a computational example.
{"title":"Determining Unfuzzy Nondominated Solutions in Combinatorial Optimization Problems with Fuzzy Costs","authors":"A. Kasperski, P. Zieliński","doi":"10.1109/FUZZY.2007.4295381","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295381","url":null,"abstract":"This paper deals with a general combinatorial optimization problem with fuzzy costs. The set of nondominated solutions with respect to an assumed fuzzy preference relation, according to the Orlovski's concept, is supposed to be the solution of the problem. A special attention is paid to the unfuzzy nondominated solutions (the solutions which are nondominated to the degree one). The main results of the paper are several new, weakened conditions on a fuzzy preference relation that allow to reduce the problem of determining unfuzzy nondominated solutions to the underling problem with deterministic costs. These solutions can be obtained by means of classical algorithms for the underling crisp problem, avoiding a construction of the special ones for the fuzzy problem. Moreover, it is shown that several known from literature fuzzy preference relations fulfill the proposed conditions. The approach is illustrated by a computational example.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132226330","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295341
J. R. Castro, O. Castillo, P. Melin
This paper presents the development and design of a graphical user interface and a command line programming toolbox for construction, edition and observation of interval type-2 fuzzy inference systems. The interval type-2 fuzzy logic system toolbox (IT2FLS), is an environment for interval type-2 fuzzy logic inference system development. Tools that cover the different phases of the fuzzy system design process, from the initial description phase, to the final implementation phase, build the toolbox. The toolbox's best qualities are the capacity to develop complex systems and the flexibility that permits the user to extend the availability of functions for working with the use of type-2 fuzzy operators, linguistic variables, interval type-2 membership functions, defuzzification methods and the evaluation of interval type-2 fuzzy inference systems.
{"title":"An Interval Type-2 Fuzzy Logic Toolbox for Control Applications","authors":"J. R. Castro, O. Castillo, P. Melin","doi":"10.1109/FUZZY.2007.4295341","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295341","url":null,"abstract":"This paper presents the development and design of a graphical user interface and a command line programming toolbox for construction, edition and observation of interval type-2 fuzzy inference systems. The interval type-2 fuzzy logic system toolbox (IT2FLS), is an environment for interval type-2 fuzzy logic inference system development. Tools that cover the different phases of the fuzzy system design process, from the initial description phase, to the final implementation phase, build the toolbox. The toolbox's best qualities are the capacity to develop complex systems and the flexibility that permits the user to extend the availability of functions for working with the use of type-2 fuzzy operators, linguistic variables, interval type-2 membership functions, defuzzification methods and the evaluation of interval type-2 fuzzy inference systems.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"146 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134161247","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295678
Chih-Ching Hsiao, S. Su
The algorithm of online predictor from input-output data pairs will be proposed. In this paper, it proposed an approach to generate fuzzy rules of predictor from real-time input-output data by means of ARMA model concept for unknown system. It includes two phase: (1). generating fuzzy rules phase, (2). online learning phase; If the error between the real output and the predictor's output is larger than the desired error, it means that the lack of the fuzzy rules. Thus, it will generate some new fuzzy rules for the fuzzy predictor or adding an output term in the premise part of fuzzy rules. From the generating fuzzy rules phase, it can online generate the fuzzy predictor. In another word, some redundant rules may be generated from bad information after learning. They may be incoming data include outliers, noises or uncertainties. Such bad rules will be discarded by a usage degree constant. To achieve good performance for this fuzzy predictor, the parameters of each fuzzy rule may be adjusted by on-line learning, when the prediction error into a pre-defined bound. In the simulation example, a nonlinear time-varying process operating in open loop is illustrated. Simulations and real-time results show the advantages of the proposed method.
{"title":"An On-Line Fuzzy Predictor from Real-Time Data","authors":"Chih-Ching Hsiao, S. Su","doi":"10.1109/FUZZY.2007.4295678","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295678","url":null,"abstract":"The algorithm of online predictor from input-output data pairs will be proposed. In this paper, it proposed an approach to generate fuzzy rules of predictor from real-time input-output data by means of ARMA model concept for unknown system. It includes two phase: (1). generating fuzzy rules phase, (2). online learning phase; If the error between the real output and the predictor's output is larger than the desired error, it means that the lack of the fuzzy rules. Thus, it will generate some new fuzzy rules for the fuzzy predictor or adding an output term in the premise part of fuzzy rules. From the generating fuzzy rules phase, it can online generate the fuzzy predictor. In another word, some redundant rules may be generated from bad information after learning. They may be incoming data include outliers, noises or uncertainties. Such bad rules will be discarded by a usage degree constant. To achieve good performance for this fuzzy predictor, the parameters of each fuzzy rule may be adjusted by on-line learning, when the prediction error into a pre-defined bound. In the simulation example, a nonlinear time-varying process operating in open loop is illustrated. Simulations and real-time results show the advantages of the proposed method.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134283397","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295524
F. Chiclana, E. Herrera-Viedma, S. Alonso, F. Herrera
The consistency of reciprocal preference relations is studied. Consistency is related with rationality, which is associated with the transitivity property. For fuzzy preference relations many properties have been suggested to model transitivity and, consequently, consistency may be measured according to which of these different properties is required to be satisfied. However, we will show that many of them are not appropriate for reciprocal preference relations. We put forward a functional equation to model consistency of reciprocal preference relations, and show that self-dual uninorms operators are the solutions to it. In particular, Tanino's multiplicative transitivity property being an example of such type of uninorms seems to be an appropriate consistency property for fuzzy reciprocal preferences.
{"title":"Consistency of Reciprocal Preference Relations","authors":"F. Chiclana, E. Herrera-Viedma, S. Alonso, F. Herrera","doi":"10.1109/FUZZY.2007.4295524","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295524","url":null,"abstract":"The consistency of reciprocal preference relations is studied. Consistency is related with rationality, which is associated with the transitivity property. For fuzzy preference relations many properties have been suggested to model transitivity and, consequently, consistency may be measured according to which of these different properties is required to be satisfied. However, we will show that many of them are not appropriate for reciprocal preference relations. We put forward a functional equation to model consistency of reciprocal preference relations, and show that self-dual uninorms operators are the solutions to it. In particular, Tanino's multiplicative transitivity property being an example of such type of uninorms seems to be an appropriate consistency property for fuzzy reciprocal preferences.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134284347","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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