Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494510
Pillt Y. Hernández, M. Melgarejo, I. S. Aguiar, Miguel A. Niño
One of the most important variables in cycling is the oxygen consumption (VO2) and its maximal value (‘VO2max’). The latter is considered as an appropriate indicator of the cardio-respiratory fitness and provides interesting information for many applications in the sports medicine context, however, this variable is not easy to measure out of a laboratory. Hence, this paper presents an alternative approach to the estimation of VO2 dynamics by means of fuzzy systems that use an input vector composed of three easy-to-obtain variables: Heart Rate, Work Rate, and Respiratory Rate measured in a clinical Cardiopulmonary-Exercise Testing. Two tuning strategies are compared: the well-known adaptive-network-based fuzzy inference system and an evolutionary fuzzy system based on the differential evolution algorithm. Experimental results showed that both tuning strategies are capable of providing competitive solutions in terms of several regression indices.
{"title":"Fuzzy Estimation of VO2 Dynamics During Cycling Exercise","authors":"Pillt Y. Hernández, M. Melgarejo, I. S. Aguiar, Miguel A. Niño","doi":"10.1109/FUZZ45933.2021.9494510","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494510","url":null,"abstract":"One of the most important variables in cycling is the oxygen consumption (VO2) and its maximal value (‘VO2max’). The latter is considered as an appropriate indicator of the cardio-respiratory fitness and provides interesting information for many applications in the sports medicine context, however, this variable is not easy to measure out of a laboratory. Hence, this paper presents an alternative approach to the estimation of VO2 dynamics by means of fuzzy systems that use an input vector composed of three easy-to-obtain variables: Heart Rate, Work Rate, and Respiratory Rate measured in a clinical Cardiopulmonary-Exercise Testing. Two tuning strategies are compared: the well-known adaptive-network-based fuzzy inference system and an evolutionary fuzzy system based on the differential evolution algorithm. Experimental results showed that both tuning strategies are capable of providing competitive solutions in terms of several regression indices.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123855784","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494463
S. Tomasiello, M. Uzair
In this paper, we introduce a variant of the Adaptive Network-based Fuzzy Inference System (ANFIS). The proposed variant does not use backpropagation and grid partitioning. Scatter partitioning is employed by complementing the least-squares method with Tikhonov regularization, both in standard and fractional version. The application example is the prediction of the burnt area in forest fires. We used two publicly available datasets for the numerical experiments. The results encourage further investigations.
{"title":"Some Remarks on ANFIS for Forest Fires Prediction","authors":"S. Tomasiello, M. Uzair","doi":"10.1109/FUZZ45933.2021.9494463","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494463","url":null,"abstract":"In this paper, we introduce a variant of the Adaptive Network-based Fuzzy Inference System (ANFIS). The proposed variant does not use backpropagation and grid partitioning. Scatter partitioning is employed by complementing the least-squares method with Tikhonov regularization, both in standard and fractional version. The application example is the prediction of the burnt area in forest fires. We used two publicly available datasets for the numerical experiments. The results encourage further investigations.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128829790","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494497
Guangzhi Ma, Feng Liu, Guangquan Zhang, Jie Lu
In the problem of multi-class classification, researchers have proved that we can train a classifier that has good performance on the test set, as long as the training and test sets are precisely drawn from the same distribution and the size of the training set approaches infinity. However, in a realworld situation, such precise observations are often unavailable in some cases. For example, readings on analogue measurement equipment are not precise numbers but intervals since there is only a finite number of decimals available. Hence, in this paper, we propose a more realistic problem called learning from imprecise observations (LIMO), where we train a classifier with fuzzy observations (i.e., fuzzy vectors). We prove the estimation error bound of this novel problem based on the distribution of fuzzy random variables. This bound demonstrates that we can always learn the best classifier when we have infinite fuzzy observations. We also develop a practical algorithm to train a classifier using fuzzy observations. The experiment results verify the efficacy of our theory and algorithm.
{"title":"Learning from Imprecise Observations: An Estimation Error Bound based on Fuzzy Random Variables","authors":"Guangzhi Ma, Feng Liu, Guangquan Zhang, Jie Lu","doi":"10.1109/FUZZ45933.2021.9494497","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494497","url":null,"abstract":"In the problem of multi-class classification, researchers have proved that we can train a classifier that has good performance on the test set, as long as the training and test sets are precisely drawn from the same distribution and the size of the training set approaches infinity. However, in a realworld situation, such precise observations are often unavailable in some cases. For example, readings on analogue measurement equipment are not precise numbers but intervals since there is only a finite number of decimals available. Hence, in this paper, we propose a more realistic problem called learning from imprecise observations (LIMO), where we train a classifier with fuzzy observations (i.e., fuzzy vectors). We prove the estimation error bound of this novel problem based on the distribution of fuzzy random variables. This bound demonstrates that we can always learn the best classifier when we have infinite fuzzy observations. We also develop a practical algorithm to train a classifier using fuzzy observations. The experiment results verify the efficacy of our theory and algorithm.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115401432","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494515
Wieslaw Paja, K. Pancerz, Barbara Pekala, J. Sarzynski
In the paper, we show how the importance of individual ranges of values of attributes describing cases can be determined using the attribute fuzzification process. The importance is determined on the basis of classification capabilities. The described approach is based mainly on fuzzy set theory and the rough set based discretization method. Moreover, an experimental study of the computer-aided classification task is presented.
{"title":"Application of the Fuzzy Logic to Evaluation and Selection of Attribute Ranges in Machine Learning","authors":"Wieslaw Paja, K. Pancerz, Barbara Pekala, J. Sarzynski","doi":"10.1109/FUZZ45933.2021.9494515","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494515","url":null,"abstract":"In the paper, we show how the importance of individual ranges of values of attributes describing cases can be determined using the attribute fuzzification process. The importance is determined on the basis of classification capabilities. The described approach is based mainly on fuzzy set theory and the rough set based discretization method. Moreover, an experimental study of the computer-aided classification task is presented.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"237 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114280174","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494580
Dawid Połap, M. Woźniak
The image recognition task is one of the fundamental aspects of image and video analysis. Recognition of individual objects allows for further inference or analysis. Unfortunately, quite often the detection and recognition itself are difficult tasks. Especially if there are many different objects in the image, or if there is some noise. In this paper, we propose a method for extracting specific features from images. The proposition is a hybridization of two main tools - meta-heuristic and fuzzy system. At first, an objective function is created for a specific object, then the meta-heuristic is used for analyzing an image for finding the best features. The operation of creating an objective function and then interpreting the position of individuals in the metaheuristic is evaluated by a fuzzy controller. The use of fuzzy logic enables the creation of decision sets during data analysis. This is possible through the adaptive technique of improving the value of the membership functions in Takagi-Sugeno systems. A fuzzy approach shows great potential in analyzing the position in the image. The proposed feature extraction mechanism has been tested and discussed due to the possibility of using fuzzy logic as well as its hybridization with meta-heuristics.
{"title":"Image features extractor based on hybridization of fuzzy controller and meta-heuristic","authors":"Dawid Połap, M. Woźniak","doi":"10.1109/FUZZ45933.2021.9494580","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494580","url":null,"abstract":"The image recognition task is one of the fundamental aspects of image and video analysis. Recognition of individual objects allows for further inference or analysis. Unfortunately, quite often the detection and recognition itself are difficult tasks. Especially if there are many different objects in the image, or if there is some noise. In this paper, we propose a method for extracting specific features from images. The proposition is a hybridization of two main tools - meta-heuristic and fuzzy system. At first, an objective function is created for a specific object, then the meta-heuristic is used for analyzing an image for finding the best features. The operation of creating an objective function and then interpreting the position of individuals in the metaheuristic is evaluated by a fuzzy controller. The use of fuzzy logic enables the creation of decision sets during data analysis. This is possible through the adaptive technique of improving the value of the membership functions in Takagi-Sugeno systems. A fuzzy approach shows great potential in analyzing the position in the image. The proposed feature extraction mechanism has been tested and discussed due to the possibility of using fuzzy logic as well as its hybridization with meta-heuristics.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126529748","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494592
Marcin Ochab, Marcin Mrukowicz, J. Sarzynski, Urszula Bentkowska
In this contribution we analyze a real DNS traffic collected at the University of Rzeszów campus. All DNS queries and responses observed in the entire network were gathered. Data include traffic generated by students, scholars, and other staff members as well as servers, IoT and all other devices connected to network. Data was collected using the Tshark network protocol analyzer and stored in a ClickHouse columnar-oriented database dedicated for high volume data analyses. Fuzzy C-means clustering was applied to analyze DNS traffic and to distinguish between human- and machine generated traffic. Analysis was performed on a representative sample containing 3 516 094 records and 33 proposed features.
{"title":"Human- and Machine-Generated Traffic Distinction by DNS Protocol Analysis","authors":"Marcin Ochab, Marcin Mrukowicz, J. Sarzynski, Urszula Bentkowska","doi":"10.1109/FUZZ45933.2021.9494592","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494592","url":null,"abstract":"In this contribution we analyze a real DNS traffic collected at the University of Rzeszów campus. All DNS queries and responses observed in the entire network were gathered. Data include traffic generated by students, scholars, and other staff members as well as servers, IoT and all other devices connected to network. Data was collected using the Tshark network protocol analyzer and stored in a ClickHouse columnar-oriented database dedicated for high volume data analyses. Fuzzy C-means clustering was applied to analyze DNS traffic and to distinguish between human- and machine generated traffic. Analysis was performed on a representative sample containing 3 516 094 records and 33 proposed features.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1244 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124925424","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494475
Nassira Achich, F. Ghorbel, F. Hamdi, Elisabeth Métais, F. Gargouri
In this paper, we propose an approach to handling uncertain time intervals and related qualitative relations, based on possibility theory. Four contributions are included in this approach. (1) Representing uncertain time intervals and related qualitative relations by extending the 4D-fluents approach with new ontological components. (2) Reasoning about uncertain time intervals by extending the Allen's interval algebra. The resulting interval relations preserve the good properties of the original algebra. (3) Proposing an OWL 2 possibilistic temporal ontology based on 4D-fluents approach extension and Allen's interval algebra extension. The proposed qualitative temporal relations are inferred via a set of SWRL rules. We validate our work by implementing a prototype based on this ontology. (4) Applying our work to PersonLink ontology and comparing the obtained results with our previous works.
{"title":"Handling Uncertain Time Intervals in OWL 2: Possibility Vs Probability Theories-based Approaches","authors":"Nassira Achich, F. Ghorbel, F. Hamdi, Elisabeth Métais, F. Gargouri","doi":"10.1109/FUZZ45933.2021.9494475","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494475","url":null,"abstract":"In this paper, we propose an approach to handling uncertain time intervals and related qualitative relations, based on possibility theory. Four contributions are included in this approach. (1) Representing uncertain time intervals and related qualitative relations by extending the 4D-fluents approach with new ontological components. (2) Reasoning about uncertain time intervals by extending the Allen's interval algebra. The resulting interval relations preserve the good properties of the original algebra. (3) Proposing an OWL 2 possibilistic temporal ontology based on 4D-fluents approach extension and Allen's interval algebra extension. The proposed qualitative temporal relations are inferred via a set of SWRL rules. We validate our work by implementing a prototype based on this ontology. (4) Applying our work to PersonLink ontology and comparing the obtained results with our previous works.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123790608","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494466
Adam Kiersztyn, Krystyna Kiersztyn, Paweł Karczmarek, M. Kaminski, I. Kitowski, Adam Zbyryt, R. Lopucki, G. Pitucha, W. Pedrycz
The selection of an appropriate method of data analysis is a key problem for researchers from various fields of applications. They consider different methods of data classification, often based on the thematic scope of the data at their disposal. However, various data characteristics, such as data set size, data type and quality, gaps, outliers and other anomalies, can make proper selection significantly difficult. Therefore, in this study we propose a method based on a very universal classifier designed on the basis of calculations using information granules. The main objective of the work is to present and comprehensively verify the effectiveness of the classifier. As an example of application, we propose complicated yet currently important data coming from widely understood ecological research. Detailed numerical experiments indicate the high efficiency of the proposed method and the possibility of easy application to data appearing in other fields. In addition, various types of aggregation functions of the classification results are considered in order to obtain the most reliable results for the discussed problems,
{"title":"Classification of Complex Ecological Objects with the Use of Information Granules","authors":"Adam Kiersztyn, Krystyna Kiersztyn, Paweł Karczmarek, M. Kaminski, I. Kitowski, Adam Zbyryt, R. Lopucki, G. Pitucha, W. Pedrycz","doi":"10.1109/FUZZ45933.2021.9494466","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494466","url":null,"abstract":"The selection of an appropriate method of data analysis is a key problem for researchers from various fields of applications. They consider different methods of data classification, often based on the thematic scope of the data at their disposal. However, various data characteristics, such as data set size, data type and quality, gaps, outliers and other anomalies, can make proper selection significantly difficult. Therefore, in this study we propose a method based on a very universal classifier designed on the basis of calculations using information granules. The main objective of the work is to present and comprehensively verify the effectiveness of the classifier. As an example of application, we propose complicated yet currently important data coming from widely understood ecological research. Detailed numerical experiments indicate the high efficiency of the proposed method and the possibility of easy application to data appearing in other fields. In addition, various types of aggregation functions of the classification results are considered in order to obtain the most reliable results for the discussed problems,","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131327033","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494551
Miriam Flores-Padilla, C. Treesatayapun
In this work, the affine equivalent model (AEM) is developed by using only the controlled systems's input-output data and it's relation based on fuzzy rules. Multi-input fuzzy rules emulated network (MiFREN) is used as function approximator when learning laws are designed to reduce the model error. Furthermore, AEM stability is guaranteed according to Lyapunov by theorem III.1. Thereafter, the control law is proposed with the information obtained by AEM. The tracking error resulted from the closed-loop system is proved as a convergent sequence by Lemma IV.1. The main advantage results in a simple control scheme and low computational cost. Numerical discrete-time systems (linear and nonlinear) are used to validate the performance of the proposed scheme altogether with the comparison results.
{"title":"Affine equivalent model based on data-driven fuzzy rules for a class of discrete-time adaptive controller","authors":"Miriam Flores-Padilla, C. Treesatayapun","doi":"10.1109/FUZZ45933.2021.9494551","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494551","url":null,"abstract":"In this work, the affine equivalent model (AEM) is developed by using only the controlled systems's input-output data and it's relation based on fuzzy rules. Multi-input fuzzy rules emulated network (MiFREN) is used as function approximator when learning laws are designed to reduce the model error. Furthermore, AEM stability is guaranteed according to Lyapunov by theorem III.1. Thereafter, the control law is proposed with the information obtained by AEM. The tracking error resulted from the closed-loop system is proved as a convergent sequence by Lemma IV.1. The main advantage results in a simple control scheme and low computational cost. Numerical discrete-time systems (linear and nonlinear) are used to validate the performance of the proposed scheme altogether with the comparison results.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126252164","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494422
D. Mougouei, A. Ghose, K. Dam, M. Fahmideh, David M. W. Powers
Requirement selection is an essential component of software release planning, which finds, for a given budget, an optimal subset of the requirements with the highest value. However, due to the dependencies among software requirements, selecting or ignoring a requirement may impact the values of others. But such Value Dependencies are imprecise and hard to capture; they have been ignored by the existing requirement selection methods, increasing the risk of value loss in software projects. To address this, we have proposed a fuzzy-based optimization method with two main components: (i) a fuzzy-based technique for modeling value dependencies and capturing their imprecision, and (ii) an Integer Linear Programming (ILP) model that takes into account value dependencies in software requirement selection. The scalability and effectiveness of the method in mitigating value loss are demonstrated through simulations.
{"title":"A Fuzzy-Based Requirement Selection Method for Considering Value Dependencies in Software Release Planning","authors":"D. Mougouei, A. Ghose, K. Dam, M. Fahmideh, David M. W. Powers","doi":"10.1109/FUZZ45933.2021.9494422","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494422","url":null,"abstract":"Requirement selection is an essential component of software release planning, which finds, for a given budget, an optimal subset of the requirements with the highest value. However, due to the dependencies among software requirements, selecting or ignoring a requirement may impact the values of others. But such Value Dependencies are imprecise and hard to capture; they have been ignored by the existing requirement selection methods, increasing the risk of value loss in software projects. To address this, we have proposed a fuzzy-based optimization method with two main components: (i) a fuzzy-based technique for modeling value dependencies and capturing their imprecision, and (ii) an Integer Linear Programming (ILP) model that takes into account value dependencies in software requirement selection. The scalability and effectiveness of the method in mitigating value loss are demonstrated through simulations.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130469937","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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