Pub Date : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851603
Arturo Garcia-Garcia, M. Reformat, Andres Mendez-Vazquez
Fuzzy Similarity Measures (FSMs) are widely used for comparison of fuzzy sets, as well as fuzzy rules. A multitude of different FSMs have been proposed so far. It is not straightforward to identify a single FSM that is the most suitable for a given task. In this paper, we investigate suitability of a few FSMs for the problem of reduction of number of rules for an image segmentation process. We use Dirichlet-based approach to generate fuzzy sets that are further used for construction of fuzzy if-then rules. We analyze similarity of these rules and select a specified number of rules for image segmentation purposes. We applied this approach to two different images.
{"title":"Similarity-based method for reduction of fuzzy rules","authors":"Arturo Garcia-Garcia, M. Reformat, Andres Mendez-Vazquez","doi":"10.1109/NAFIPS.2016.7851603","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851603","url":null,"abstract":"Fuzzy Similarity Measures (FSMs) are widely used for comparison of fuzzy sets, as well as fuzzy rules. A multitude of different FSMs have been proposed so far. It is not straightforward to identify a single FSM that is the most suitable for a given task. In this paper, we investigate suitability of a few FSMs for the problem of reduction of number of rules for an image segmentation process. We use Dirichlet-based approach to generate fuzzy sets that are further used for construction of fuzzy if-then rules. We analyze similarity of these rules and select a specified number of rules for image segmentation purposes. We applied this approach to two different images.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121823223","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851595
R. Rocha, F. Gomide
Evolving systems and high dimensional stream data processing algorithms are of enormous practical importance and currently are under intensive investigation. This paper introduces an evolving neural classifier approach and evaluates its performance using high dimensional data and evolving and classic classifier algorithms representative of the current state of the art. The proposed approach works in one-pass mode to simultaneously find the neural network structure and its weights using high dimensional stream data. The results suggests that the classification rate achieved by the proposed approach is very competitive with the evolving models addressed in this paper. It outperforms all of them in most of the datasets considered. Also, the approach requires the lowest per sample processing time amongst the evolving and classic batch classifiers.
{"title":"Performance evaluation of evolving classifier algorithms in high dimensional spaces","authors":"R. Rocha, F. Gomide","doi":"10.1109/NAFIPS.2016.7851595","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851595","url":null,"abstract":"Evolving systems and high dimensional stream data processing algorithms are of enormous practical importance and currently are under intensive investigation. This paper introduces an evolving neural classifier approach and evaluates its performance using high dimensional data and evolving and classic classifier algorithms representative of the current state of the art. The proposed approach works in one-pass mode to simultaneously find the neural network structure and its weights using high dimensional stream data. The results suggests that the classification rate achieved by the proposed approach is very competitive with the evolving models addressed in this paper. It outperforms all of them in most of the datasets considered. Also, the approach requires the lowest per sample processing time amongst the evolving and classic batch classifiers.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131494480","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851633
Hoda Safaeipour, M. F. Zarandi, S. Bastani
Fuzzy ontology is a generalization of crisp ontology for modeling uncertain information and has been applied in recent years for supporting different activities of semantic web. However, there are great collections of crisp ontologies developed so far in various domains which are not appropriate for decision making in fuzzy environment. Accordingly, this paper aims at presenting an approach to automatically convert a crisp ontology to fuzzy ontology in the context of social networks. Furthermore, this paper demonstrates that the combination of a learning process of crisp ontology with proposed approach, decreases computational complexity of fuzzy ontology learning due to breaking the task to two optimal steps. Accordingly, the approach allows for an advantageous application of various crisp clustering techniques in fuzzy ontology context.
{"title":"Crisp to fuzzy ontology conversion in the context of social networks: A new approach","authors":"Hoda Safaeipour, M. F. Zarandi, S. Bastani","doi":"10.1109/NAFIPS.2016.7851633","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851633","url":null,"abstract":"Fuzzy ontology is a generalization of crisp ontology for modeling uncertain information and has been applied in recent years for supporting different activities of semantic web. However, there are great collections of crisp ontologies developed so far in various domains which are not appropriate for decision making in fuzzy environment. Accordingly, this paper aims at presenting an approach to automatically convert a crisp ontology to fuzzy ontology in the context of social networks. Furthermore, this paper demonstrates that the combination of a learning process of crisp ontology with proposed approach, decreases computational complexity of fuzzy ontology learning due to breaking the task to two optimal steps. Accordingly, the approach allows for an advantageous application of various crisp clustering techniques in fuzzy ontology context.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131561243","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851608
H. Alhumaidi
This paper introduces a quantitative fuzzy weighted average method to assist contractors in deciding whether to bid on a project by simulating a multiple-criteria decision making process that integrates a group of several decision-makers. The triangular fuzzy-set model is implemented to define linguistic terms used to describe subjective judgments related to decision-makers' experience level, criteria weight assessment and selection criteria rating. This paper provides an illustrative case study of project selection to demonstrate its effectiveness. The proposed method applied here to construction projects can be implemented for any type of project in any geographic area.
{"title":"Fuzzy weighted average approach to ranking projects in contractor initial bidding","authors":"H. Alhumaidi","doi":"10.1109/NAFIPS.2016.7851608","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851608","url":null,"abstract":"This paper introduces a quantitative fuzzy weighted average method to assist contractors in deciding whether to bid on a project by simulating a multiple-criteria decision making process that integrates a group of several decision-makers. The triangular fuzzy-set model is implemented to define linguistic terms used to describe subjective judgments related to decision-makers' experience level, criteria weight assessment and selection criteria rating. This paper provides an illustrative case study of project selection to demonstrate its effectiveness. The proposed method applied here to construction projects can be implemented for any type of project in any geographic area.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116508632","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851618
Mahdokht Afravi, V. Kreinovich
In expert systems, we often face a problem of estimating the expert's degree of confidence in a composite statement A&B based on the known expert's degrees of confidence a = d(A) and b = d(B) in individual statements A and B. The corresponding estimate f&(a, b) is sometimes called an “and”-operation. Traditional fuzzy logic assumes that the same “and”-operation is applied to all pairs of statements. In this case, it is reasonable to justify that the “and”-operation be associative; such “and”-operations are known as t-norms. In practice, however, in different areas, different “and”-operations provide a good description of expert reasoning. As a result, when we combine expert knowledge from different areas into a single expert system, it is reasonable to use different “and”-operations to combine different statements. In this case, associativity is no longer a natural requirement. We show, however, that in such situations, under some reasonable conditions, associativity of each “and”-operation can still be deduced. Thus, in this case, we can still use associative t-norms.
在专家系统中，我们经常面临这样一个问题：根据已知专家对单个语句 A 和 B 的置信度 a = d(A)和 b = d(B)，估计专家对综合语句 A&B 的置信度。传统的模糊逻辑假定所有成对的语句都采用相同的 "和 "运算。在这种情况下，有理由认为 "和 "运算是关联的；这种 "和 "运算被称为 t 规范。但实际上，在不同的领域，不同的 "和 "操作可以很好地描述专家推理。因此，当我们把不同领域的专家知识整合到一个专家系统中时，使用不同的 "和 "操作来组合不同的语句是合理的。在这种情况下，关联性不再是一个自然要求。不过，我们证明，在这种情况下，在一些合理的条件下，每个 "和 "操作的关联性仍然可以推导出来。因此，在这种情况下，我们仍然可以使用关联 t 规范。
{"title":"What if we use different “and”-operations in the same expert system","authors":"Mahdokht Afravi, V. Kreinovich","doi":"10.1109/NAFIPS.2016.7851618","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851618","url":null,"abstract":"In expert systems, we often face a problem of estimating the expert's degree of confidence in a composite statement A&B based on the known expert's degrees of confidence a = d(A) and b = d(B) in individual statements A and B. The corresponding estimate f&(a, b) is sometimes called an “and”-operation. Traditional fuzzy logic assumes that the same “and”-operation is applied to all pairs of statements. In this case, it is reasonable to justify that the “and”-operation be associative; such “and”-operations are known as t-norms. In practice, however, in different areas, different “and”-operations provide a good description of expert reasoning. As a result, when we combine expert knowledge from different areas into a single expert system, it is reasonable to use different “and”-operations to combine different statements. In this case, associativity is no longer a natural requirement. We show, however, that in such situations, under some reasonable conditions, associativity of each “and”-operation can still be deduced. Thus, in this case, we can still use associative t-norms.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132656152","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851609
L. D. Nguyen, Dai Q. Tran, An T. Nguyen, Long Le-Hoai
As construction projects are increasingly complex, a systematic approach for assessing their complexity is imperative. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) method was employed to determine the local and global weights of the criteria and sub-criteria in project complexity. The overall project complexity is quantified by a proposed measure, named complexity level (CL). A computational model was developed in MATLAB to facilitate calculations in Fuzzy AHP, conduct sensitivity analysis, and visualize results. The application of the model was illustrated in a case study of three transportation projects performed by a heavy construction company. The proposed complexity level enables engineers and managers to better anticipate potential difficulties in their complex construction projects. Scarce resources will be therefore allocated efficiently in various construction projects in a company's portfolio.
{"title":"Computational model for measuring project complexity in construction","authors":"L. D. Nguyen, Dai Q. Tran, An T. Nguyen, Long Le-Hoai","doi":"10.1109/NAFIPS.2016.7851609","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851609","url":null,"abstract":"As construction projects are increasingly complex, a systematic approach for assessing their complexity is imperative. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) method was employed to determine the local and global weights of the criteria and sub-criteria in project complexity. The overall project complexity is quantified by a proposed measure, named complexity level (CL). A computational model was developed in MATLAB to facilitate calculations in Fuzzy AHP, conduct sensitivity analysis, and visualize results. The application of the model was illustrated in a case study of three transportation projects performed by a heavy construction company. The proposed complexity level enables engineers and managers to better anticipate potential difficulties in their complex construction projects. Scarce resources will be therefore allocated efficiently in various construction projects in a company's portfolio.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134376713","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851624
H. Nguyen
The aim of the paper is presenting an approach to developing a fuzzy rule based expert system shell combin-ing positive and negative knowledge for medical consultations called FuzzRESS. We extend Max-Min inference of CADIAG-2 like systems [3] by replacing Max of MaxMin rules by t-conorm and by including negative knowledge. Based on this approach, we propose a structure of FuzzRESS which consists of some main components: rule base, inference engine, explanation, interface and knowledge acquisition. The system FuzzRESS is implemented and demonstrated with diagnosis of Fever according to internal traditional medicine.
{"title":"FuzzRESS: A fuzzy rule-based expert system shell combining positive and negative knowledge for consultation of Vietnamese traditional medicine","authors":"H. Nguyen","doi":"10.1109/NAFIPS.2016.7851624","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851624","url":null,"abstract":"The aim of the paper is presenting an approach to developing a fuzzy rule based expert system shell combin-ing positive and negative knowledge for medical consultations called FuzzRESS. We extend Max-Min inference of CADIAG-2 like systems [3] by replacing Max of MaxMin rules by t-conorm and by including negative knowledge. Based on this approach, we propose a structure of FuzzRESS which consists of some main components: rule base, inference engine, explanation, interface and knowledge acquisition. The system FuzzRESS is implemented and demonstrated with diagnosis of Fever according to internal traditional medicine.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122209332","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851630
Hosein Hamisheh Bahar, M. Zarandi, A. Esfahanipour
In this paper, an expert system is developed using fuzzy genetic network programming with reinforcement learning (GNP-RL) in order to generate stock trading signals based on technical indices of the stock prices. In order to increase the accuracy and reliability of results, we applied Wavelet Transform to eliminate noises and irregularities in prices. Since choosing the most appropriate wavelet base is an important decision, the Energy to Shannon Entropy Ratio, as an objective method, is used in order to address this concern. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. The proposed model has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in testing time period shows that the developed system has more favorable performance in comparison with the simple GNP-RL with binary signals and Buy and Hold strategy.
{"title":"Generating ternary stock trading signals using fuzzy genetic network programming","authors":"Hosein Hamisheh Bahar, M. Zarandi, A. Esfahanipour","doi":"10.1109/NAFIPS.2016.7851630","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851630","url":null,"abstract":"In this paper, an expert system is developed using fuzzy genetic network programming with reinforcement learning (GNP-RL) in order to generate stock trading signals based on technical indices of the stock prices. In order to increase the accuracy and reliability of results, we applied Wavelet Transform to eliminate noises and irregularities in prices. Since choosing the most appropriate wavelet base is an important decision, the Energy to Shannon Entropy Ratio, as an objective method, is used in order to address this concern. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. The proposed model has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in testing time period shows that the developed system has more favorable performance in comparison with the simple GNP-RL with binary signals and Buy and Hold strategy.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117137250","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851611
N. Siraj, M. Omar, A. Fayek
In Multi-Criteria Group Decision Making (MCGDM) problems, aggregation and consensus methods are two key elements in reaching an overall collective value representing the group of experts making the decision. In many instances, the assessment process is based on linguistic terms rather than numerical values. Therefore, fuzzy aggregation and fuzzy consensus methods are more suitable for dealing with the linguistic terms used to reach the final decision. First, we present fuzzy set theory, fuzzy aggregation, and fuzzy consensus. Then, we describe a process for integrating fuzzy aggregation and fuzzy consensus in group decision-making problems. This process considers the aggregation of multiple criteria used for evaluation as well as the degree of consensus between the experts. Finally, we present a hypothetical case study to implement the developed process in MCGDM related to contractor selection. This paper contributes to the body of knowledge by developing a process that applies fuzzy aggregation and fuzzy consensus in solving MCGDM problems in construction. Furthermore, through the application of fuzzy set theory in aggregation and consensus, the developed process assists decision makers in problems that encompass subjectivity and uncertainty in their assessment.
{"title":"A combined fuzzy aggregation and consensus process for Multi-Criteria Group Decision Making problems","authors":"N. Siraj, M. Omar, A. Fayek","doi":"10.1109/NAFIPS.2016.7851611","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851611","url":null,"abstract":"In Multi-Criteria Group Decision Making (MCGDM) problems, aggregation and consensus methods are two key elements in reaching an overall collective value representing the group of experts making the decision. In many instances, the assessment process is based on linguistic terms rather than numerical values. Therefore, fuzzy aggregation and fuzzy consensus methods are more suitable for dealing with the linguistic terms used to reach the final decision. First, we present fuzzy set theory, fuzzy aggregation, and fuzzy consensus. Then, we describe a process for integrating fuzzy aggregation and fuzzy consensus in group decision-making problems. This process considers the aggregation of multiple criteria used for evaluation as well as the degree of consensus between the experts. Finally, we present a hypothetical case study to implement the developed process in MCGDM related to contractor selection. This paper contributes to the body of knowledge by developing a process that applies fuzzy aggregation and fuzzy consensus in solving MCGDM problems in construction. Furthermore, through the application of fuzzy set theory in aggregation and consensus, the developed process assists decision makers in problems that encompass subjectivity and uncertainty in their assessment.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124960149","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 : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851596
H. R. D. N. Costa, A. L. Neve
This paper presents the application of soft computing techniques to a central air conditioning system aimed at efficient energy consumption. The current buildings have automation systems that provide information about the lighting, electrical system, air conditioning system etc. We studied the air conditioning system, in particular with a view to efficient energy consumption. The air conditioning system had priority in this study because its energy consumption is high.. The results of the applications were compared with the application of PID controllers, and fuzzy control system for a central air conditioning system.
{"title":"Modeling of an air conditioning system through techniques of soft-computing","authors":"H. R. D. N. Costa, A. L. Neve","doi":"10.1109/NAFIPS.2016.7851596","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851596","url":null,"abstract":"This paper presents the application of soft computing techniques to a central air conditioning system aimed at efficient energy consumption. The current buildings have automation systems that provide information about the lighting, electrical system, air conditioning system etc. We studied the air conditioning system, in particular with a view to efficient energy consumption. The air conditioning system had priority in this study because its energy consumption is high.. The results of the applications were compared with the application of PID controllers, and fuzzy control system for a central air conditioning system.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126541724","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：