Pub Date : 2020-08-01DOI: 10.1109/IS48319.2020.9199963
A. R. G. Filho, Filipe de S. L. Ribeiro, R. Carvalho, C. Coelho
The hill chart is an important tool for the study of the turbine performance, the energy production as well as management and hydropower control. This paper propose a model to generate two hill chart based on feed-forward Artificial Neural Network (ANN-FF). The dataset used for training the ANN-FF model is obtained from a small-scale test model of hydroelectric turbine, installed on the Madeira River in the state of Rondonia, Brazil. Predicted values obtained by applying the proposed ANN-FF model for each parameter is similar to the values measured from the small-scale test model of the turbine. The training errors of the proposed ANN-FF model have significant values from the third decimal point. It is concluded that ANN-FF is a good strategy for the generation of hill charts for the study of hydroelectric turbine efficiency.
{"title":"Generation of Two Turbine Hill Chart Using Artificial Neural Networks","authors":"A. R. G. Filho, Filipe de S. L. Ribeiro, R. Carvalho, C. Coelho","doi":"10.1109/IS48319.2020.9199963","DOIUrl":"https://doi.org/10.1109/IS48319.2020.9199963","url":null,"abstract":"The hill chart is an important tool for the study of the turbine performance, the energy production as well as management and hydropower control. This paper propose a model to generate two hill chart based on feed-forward Artificial Neural Network (ANN-FF). The dataset used for training the ANN-FF model is obtained from a small-scale test model of hydroelectric turbine, installed on the Madeira River in the state of Rondonia, Brazil. Predicted values obtained by applying the proposed ANN-FF model for each parameter is similar to the values measured from the small-scale test model of the turbine. The training errors of the proposed ANN-FF model have significant values from the third decimal point. It is concluded that ANN-FF is a good strategy for the generation of hill charts for the study of hydroelectric turbine efficiency.","PeriodicalId":129583,"journal":{"name":"IEEE Conf. on Intelligent Systems","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116293657","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}
In this paper, two methodologies of data-driven fuzzy modelling for multivariable nonlinear systems based on Observer/Kalman Filter Identification (OKID) and the Eigensystem Realization Algorithm (ERA) are proposed. The multivariable nonlinear system is represented by a fuzzy Takagi-Sugeno (TS) model, whose antecedent is constituted by linguistic variables (fuzzy sets) and the consequent is constituted by linear submodels in state-space discrete representation. The antecedent parameters are obtained using clustering fuzzy algorithms and the consequent parameters (state matrix, input matrix, output matrix and direct transition matrix) are obtained using the algorithm discussed in this article. Experimental results for identification of a Quadrotor Unmanned Aerial Vehicle (UAV) are presented, in order to illustrate the efficiency and applicability of the methodologies in real systems with coupled data and real systems with decoupled data.
{"title":"Data-Driven Fuzzy Modelling Methodologies for Multivariable Nonlinear Systems","authors":"J. S. Junior, E. B. M. Costa","doi":"10.1109/IS.2018.8710486","DOIUrl":"https://doi.org/10.1109/IS.2018.8710486","url":null,"abstract":"In this paper, two methodologies of data-driven fuzzy modelling for multivariable nonlinear systems based on Observer/Kalman Filter Identification (OKID) and the Eigensystem Realization Algorithm (ERA) are proposed. The multivariable nonlinear system is represented by a fuzzy Takagi-Sugeno (TS) model, whose antecedent is constituted by linguistic variables (fuzzy sets) and the consequent is constituted by linear submodels in state-space discrete representation. The antecedent parameters are obtained using clustering fuzzy algorithms and the consequent parameters (state matrix, input matrix, output matrix and direct transition matrix) are obtained using the algorithm discussed in this article. Experimental results for identification of a Quadrotor Unmanned Aerial Vehicle (UAV) are presented, in order to illustrate the efficiency and applicability of the methodologies in real systems with coupled data and real systems with decoupled data.","PeriodicalId":129583,"journal":{"name":"IEEE Conf. on Intelligent Systems","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115701271","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 : 2016-02-01DOI: 10.1007/978-3-319-11310-4_5
K. Yakovlev, Vsevolod Khithov, M. Loginov, A. Petrov
{"title":"Distributed Control and Navigation System for Quadrotor UAVs in GPS-Denied Environments","authors":"K. Yakovlev, Vsevolod Khithov, M. Loginov, A. Petrov","doi":"10.1007/978-3-319-11310-4_5","DOIUrl":"https://doi.org/10.1007/978-3-319-11310-4_5","url":null,"abstract":"","PeriodicalId":129583,"journal":{"name":"IEEE Conf. on Intelligent Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132003636","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 : 1900-01-01DOI: 10.1007/978-3-319-11310-4_18
M. Andrzejczak, M. Ulinowicz
{"title":"Filtration and Integration System (FIS) for Navigation Data Processing Based on Kalman Filter","authors":"M. Andrzejczak, M. Ulinowicz","doi":"10.1007/978-3-319-11310-4_18","DOIUrl":"https://doi.org/10.1007/978-3-319-11310-4_18","url":null,"abstract":"","PeriodicalId":129583,"journal":{"name":"IEEE Conf. on Intelligent Systems","volume":"56 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125911957","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 : 1900-01-01DOI: 10.1007/978-3-319-11313-5_20
W. Homenda, W. Pedrycz
{"title":"Linguistic Approach to Granular Cognitive Maps - User's Tool for Knowledge Accessing and Processing","authors":"W. Homenda, W. Pedrycz","doi":"10.1007/978-3-319-11313-5_20","DOIUrl":"https://doi.org/10.1007/978-3-319-11313-5_20","url":null,"abstract":"","PeriodicalId":129583,"journal":{"name":"IEEE Conf. on Intelligent Systems","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114386950","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 : 1900-01-01DOI: 10.1007/978-3-319-11310-4_31
T. Pander, R. Czabański, T. Przybyla, E. Straszecka
{"title":"The Application of Median Fuzzy Clustering and Robust Weighted Averaging for Electronystagmography Signal Processing","authors":"T. Pander, R. Czabański, T. Przybyla, E. Straszecka","doi":"10.1007/978-3-319-11310-4_31","DOIUrl":"https://doi.org/10.1007/978-3-319-11310-4_31","url":null,"abstract":"","PeriodicalId":129583,"journal":{"name":"IEEE Conf. on Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129731244","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 : 1900-01-01DOI: 10.1007/978-3-319-11310-4_67
S. Grubwinkler, M. Lienkamp
{"title":"Energy Prediction for EVs Using Support Vector Regression Methods","authors":"S. Grubwinkler, M. Lienkamp","doi":"10.1007/978-3-319-11310-4_67","DOIUrl":"https://doi.org/10.1007/978-3-319-11310-4_67","url":null,"abstract":"","PeriodicalId":129583,"journal":{"name":"IEEE Conf. on Intelligent Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123504833","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 : 1900-01-01DOI: 10.1007/978-3-319-11310-4_76
A. S. Hussein, Ibrahim M. Hamed, M. Tolba
{"title":"An Efficient System for Stock Market Prediction","authors":"A. S. Hussein, Ibrahim M. Hamed, M. Tolba","doi":"10.1007/978-3-319-11310-4_76","DOIUrl":"https://doi.org/10.1007/978-3-319-11310-4_76","url":null,"abstract":"","PeriodicalId":129583,"journal":{"name":"IEEE Conf. on Intelligent Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121196876","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 : 1900-01-01DOI: 10.1007/978-3-319-11313-5_5
E. Rak
{"title":"The Modularity Equation in the Class of 2-uninorms","authors":"E. Rak","doi":"10.1007/978-3-319-11313-5_5","DOIUrl":"https://doi.org/10.1007/978-3-319-11313-5_5","url":null,"abstract":"","PeriodicalId":129583,"journal":{"name":"IEEE Conf. on Intelligent Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124385285","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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