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Dr. KATARZYNA POCZETA is an Assistant Professor in the Department of Information Systems at Kielce University of Technology, Poland. She received a PhD in Computer Science from Lodz University of Technology, Poland (April 2015). She has been working for ten years as researcher in statutory projects of the Department of Information Systems at Kielce University of Technology, Poland. She was a head of two research projects for the development of young researchers (2014-2019). Dr. Katarzyna Poczeta is author and co-author of 49 publications in journals, conference papers and book chapters (h-index=8 in scopus and h-index=9 in googlescholar). She is also a reviewer in many international journals, e.g. IEEE Transactions on Fuzzy Systems, Neural Computing and Applications, Neurocomputing. Her research interests include decision support systems, fuzzy cognitive maps, artificial neural networks, evolutionary computation, time series prediction, machine learning and data mining.
Representing and analyzing the complexity of models constructed by data is a difficult and challenging task, hence the need for new, more effective techniques emerges, despite the numerous methodologies recently proposed in this field. In the present paper, the main idea is to systematically create a nested structure, based on a fuzzy cognitive map (FCM), in which each element/concept at a higher map level is decomposed into another FCM that provides a more detailed and precise representation of complex time series data. This nested structure is then optimized by applying evolutionary learning algorithms. Through the application of a dynamic optimization process, the whole nested structure based on FCMs is restructured in order to derive important relationships between map concepts at every nesting level as well as to determine the weights of these relationships on the basis of the available time series. This process allows discovering and describing hidden relationships among important map concepts. The paper proposes the application of the suggested nested approach for time series forecasting as well as for decision-making tasks regarding appliances’ energy consumption prediction.
Katarzyna Poczeta; Elpiniki I. Papageorgiou; Vassilis C. Gerogiannis. Fuzzy Cognitive Maps Optimization for Decision Making and Prediction. Mathematics 2020, 8, 2059 .
AMA StyleKatarzyna Poczeta, Elpiniki I. Papageorgiou, Vassilis C. Gerogiannis. Fuzzy Cognitive Maps Optimization for Decision Making and Prediction. Mathematics. 2020; 8 (11):2059.
Chicago/Turabian StyleKatarzyna Poczeta; Elpiniki I. Papageorgiou; Vassilis C. Gerogiannis. 2020. "Fuzzy Cognitive Maps Optimization for Decision Making and Prediction." Mathematics 8, no. 11: 2059.
(1) Background: Forecasting of energy consumption demand is a crucial task linked directly with the economy of every country all over the world. Accurate natural gas consumption forecasting allows policy makers to formulate natural gas supply planning and apply the right strategic policies in this direction. In order to develop a real accurate natural gas (NG) prediction model for Greece, we examine the application of neuro-fuzzy models, which have recently shown significant contribution in the energy domain. (2) Methods: The adaptive neuro-fuzzy inference system (ANFIS) is a flexible and easy to use modeling method in the area of soft computing, integrating both neural networks and fuzzy logic principles. The present study aims to develop a proper ANFIS architecture for time series modeling and prediction of day-ahead natural gas demand. (3) Results: An efficient and fast ANFIS architecture is built based on neuro-fuzzy exploration performance for energy demand prediction using historical data of natural gas consumption, achieving a high prediction accuracy. The best performing ANFIS method is also compared with other well-known artificial neural networks (ANNs), soft computing methods such as fuzzy cognitive map (FCM) and their hybrid combination architectures for natural gas prediction, reported in the literature, to further assess its prediction performance. The conducted analysis reveals that the mean absolute percentage error (MAPE) of the proposed ANFIS architecture results is less than 20% in almost all the examined Greek cities, outperforming ANNs, FCMs and their hybrid combination; and (4) Conclusions: The produced results reveal an improved prediction efficacy of the proposed ANFIS-based approach for the examined natural gas case study in Greece, thus providing a fast and efficient tool for utterly accurate predictions of future short-term natural gas demand.
Konstantinos Papageorgiou; Elpiniki I. Papageorgiou; Katarzyna Poczeta; Dionysis Bochtis; George Stamoulis. Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System. Energies 2020, 13, 2317 .
AMA StyleKonstantinos Papageorgiou, Elpiniki I. Papageorgiou, Katarzyna Poczeta, Dionysis Bochtis, George Stamoulis. Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System. Energies. 2020; 13 (9):2317.
Chicago/Turabian StyleKonstantinos Papageorgiou; Elpiniki I. Papageorgiou; Katarzyna Poczeta; Dionysis Bochtis; George Stamoulis. 2020. "Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System." Energies 13, no. 9: 2317.
The paper concerns the use of fuzzy cognitive maps and k-means clustering to solve the problem of modeling multidimensional medical data. A fuzzy cognitive map is a recurrent neural network that describes the analyzed phenomenon in the form of key concepts and causal relationships between them. It is an effective tool for modeling decision support systems and is widely used in medicine. The aim of this paper is to analyze the use of fuzzy cognitive maps with k-means clustering to model decision support systems based on multidimensional data related to Parkinson’s disease. K-means method was applied to group the data, and then a separate fuzzy cognitive map was built for each cluster to increase forecasting accuracy. The learning process was realized with the use of the previously developed Individually Directional Evolutionary Algorithm. The obtained results confirm that the analyzed approach provides much better forecasting accuracy than the standard approach based on one model.
Katarzyna Poczeta; Łukasz Kubuś; Alexander Yastrebov. Multidimensional medical data modeling based on fuzzy cognitive maps and k-means clustering. Procedia Computer Science 2020, 176, 118 -127.
AMA StyleKatarzyna Poczeta, Łukasz Kubuś, Alexander Yastrebov. Multidimensional medical data modeling based on fuzzy cognitive maps and k-means clustering. Procedia Computer Science. 2020; 176 ():118-127.
Chicago/Turabian StyleKatarzyna Poczeta; Łukasz Kubuś; Alexander Yastrebov. 2020. "Multidimensional medical data modeling based on fuzzy cognitive maps and k-means clustering." Procedia Computer Science 176, no. : 118-127.
The fuzzy cognitive map (FCM) is an effective tool for modeling dynamic decision support systems. It describes the analyzed phenomenon in the form of key concepts and the causal connections between them. The main aspects of the building of the FCM model are: concepts selection, determining the output concepts, criterion selection, and determining the relationships between concepts. It is usually based on expert knowledge. The main goal of the paper is to define the optimal in some sense FCM structure through the introduction of the notion of output concepts and minimizing the number of concepts and connections between them. The proposed approach allows for: (1) the selection of key concepts based on graph theory metrics and determining the connections between them; (2) the determination of the criterion of learning based on output concepts and fitting the learning process to the analyzed problem. A simulation analysis was done with the use of synthetic and real-life data. Experiments confirm that the proposed approach improves the learning process compared to the standard approaches.
Katarzyna Poczeta; Łukasz Kubuś; Alexander Yastrebov. Reprint of: Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts. Biosystems 2019, 186, 104068 .
AMA StyleKatarzyna Poczeta, Łukasz Kubuś, Alexander Yastrebov. Reprint of: Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts. Biosystems. 2019; 186 ():104068.
Chicago/Turabian StyleKatarzyna Poczeta; Łukasz Kubuś; Alexander Yastrebov. 2019. "Reprint of: Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts." Biosystems 186, no. : 104068.
The paper concerns the use of evolutionary algorithms to solve the problem of multiobjective optimization and learning of fuzzy cognitive maps (FCMs) on the basis of multidimensional medical data related to diabetes. The aim of this research study is an automatic construction of a collection of FCM models based on various criteria depending on the structure of the model and forecasting capabilities. The simulation analysis was performed with the use of the developed multiobjective Individually Directional Evolutionary Algorithm. Experiments show that the collection of fuzzy cognitive maps, in which each element is built on the basis of particular patient data, allows us to receive higher forecasting accuracy compared to the standard approach. Moreover, by appropriate aggregation of these collections we can also obtain satisfactory accuracy of forecasts for the new patient.
Alexander Yastrebov; Łukasz Kubuś; Katarzyna Poczeta. An Analysis of Evolutionary Algorithms for Multiobjective Optimization of Structure and Learning of Fuzzy Cognitive Maps Based on Multidimensional Medical Data. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 147 -158.
AMA StyleAlexander Yastrebov, Łukasz Kubuś, Katarzyna Poczeta. An Analysis of Evolutionary Algorithms for Multiobjective Optimization of Structure and Learning of Fuzzy Cognitive Maps Based on Multidimensional Medical Data. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():147-158.
Chicago/Turabian StyleAlexander Yastrebov; Łukasz Kubuś; Katarzyna Poczeta. 2019. "An Analysis of Evolutionary Algorithms for Multiobjective Optimization of Structure and Learning of Fuzzy Cognitive Maps Based on Multidimensional Medical Data." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 147-158.
This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future data from analyzing records of data. In the paper, we proposed an ensemble-based forecast combination methodology as an alternative approach to forecasting methods for time series prediction. The ensemble learning technique combines various learning algorithms, including SOGA (structure optimization genetic algorithm)-based FCMs, RCGA (real coded genetic algorithm)-based FCMs, efficient and adaptive ANNs architectures, and a hybrid structure of FCM-ANN, recently proposed for time series forecasting. All ensemble algorithms execute according to the one-step prediction regime. The particular forecast combination approach was specifically selected due to the advanced features of each ensemble component, where the findings of this work evinced the effectiveness of this approach, in terms of prediction accuracy, when compared against other well-known, independent forecasting approaches, such as ANNs or FCMs, and the long short-term memory (LSTM) algorithm as well. The suggested ensemble learning approach was applied to three distribution points that compose the natural gas grid of a Greek region. For the evaluation of the proposed approach, a real-time series dataset for natural gas prediction was used. We also provided a detailed discussion on the performance of the individual predictors, the ensemble predictors, and their combination through two well-known ensemble methods (the average and the error-based) that are characterized in the literature as particularly accurate and effective. The prediction results showed the efficacy of the proposed ensemble learning approach, and the comparative analysis demonstrated enough evidence that the approach could be used effectively to conduct forecasting based on multivariate time series.
Elpiniki Papageorgiou; Katarzyna Poczeta; Vassilis C. Gerogiannis; George Stamoulis. Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece. Algorithms 2019, 12, 235 .
AMA StyleElpiniki Papageorgiou, Katarzyna Poczeta, Vassilis C. Gerogiannis, George Stamoulis. Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece. Algorithms. 2019; 12 (11):235.
Chicago/Turabian StyleElpiniki Papageorgiou; Katarzyna Poczeta; Vassilis C. Gerogiannis; George Stamoulis. 2019. "Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece." Algorithms 12, no. 11: 235.
The fuzzy cognitive map (FCM) is an effective tool for modeling dynamic decision support systems. It describes the analyzed phenomenon in the form of key concepts and the causal connections between them. The main aspects of the building of the FCM model are: concepts selection, determining the output concepts, criterion selection, and determining the relationships between concepts. It is usually based on expert knowledge. The main goal of the paper is to define the optimal in some sense FCM structure through the introduction of the notion of output concepts and minimizing the number of concepts and connections between them. The proposed approach allows for: 1) the selection of key concepts based on graph theory metrics and determining the connections between them; 2) the determination of the criterion of learning based on output concepts and fitting the learning process to the analyzed problem. A simulation analysis was done with the use of synthetic and real-life data. Experiments confirm that the proposed approach improves the learning process compared to the standard approaches.
Katarzyna Poczeta; Łukasz Kubuś; Alexander Yastrebov. Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts. Biosystems 2019, 179, 39 -47.
AMA StyleKatarzyna Poczeta, Łukasz Kubuś, Alexander Yastrebov. Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts. Biosystems. 2019; 179 ():39-47.
Chicago/Turabian StyleKatarzyna Poczeta; Łukasz Kubuś; Alexander Yastrebov. 2019. "Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts." Biosystems 179, no. : 39-47.
Fuzzy cognitive map (FCM) allows to discover knowledge in the form of concepts significant for the analyzed problem and causal connections between them. The FCM model can be developed by experts or using learning algorithms and available data. The main aspect of building of the FCM model is concepts selection. It is usually based on the expert knowledge. The aim of this paper is to present the developed evolutionary algorithm for structure optimization and learning of fuzzy cognitive map on the basis of available data. The proposed approach allows to select key concepts during learning process based on metrics from the area of graph theory: significance of each node, total value of a node and total influence of the concept and determine the weights of the connections between them. A simulation analysis of the developed algorithm was done with the use of synthetic and real-life data.
Katarzyna Poczeta; Łukasz Kubuś; Alexander Yastrebov. Structure Optimization and Learning of Fuzzy Cognitive Map with the Use of Evolutionary Algorithm and Graph Theory Metrics. Econometrics for Financial Applications 2018, 131 -147.
AMA StyleKatarzyna Poczeta, Łukasz Kubuś, Alexander Yastrebov. Structure Optimization and Learning of Fuzzy Cognitive Map with the Use of Evolutionary Algorithm and Graph Theory Metrics. Econometrics for Financial Applications. 2018; ():131-147.
Chicago/Turabian StyleKatarzyna Poczeta; Łukasz Kubuś; Alexander Yastrebov. 2018. "Structure Optimization and Learning of Fuzzy Cognitive Map with the Use of Evolutionary Algorithm and Graph Theory Metrics." Econometrics for Financial Applications , no. : 131-147.
One of the way to save energy in smart buildings is the prediction of the variables that affect energy consumption. The aim of this paper is the application of fuzzy cognitive map for indoor temperature forecasting. Fuzzy cognitive map is a soft computing technique that describes the analyzed problem as a set of concepts and connections between them. The developed evolutionary algorithm for fuzzy cognitive maps learning is used to select the most significant concepts (sensors in a smart building) and determine the weights of the connections. The data captured in the SMLsystem created at the Universidad CEU Cardenal Herrera for participation in the Solar Decathlon 2013 competition were used in the experiments. Results show a high forecasting accuracy and they could be used to control smart building and to reduce the number of required sensors.
Katarzyna Poczęta; Łukasz Kubuś; Alexander Yastrebov; Elpiniki I. Papageorgiou. Temperature Forecasting for Energy Saving in Smart Buildings Based on Fuzzy Cognitive Map. Advances in Intelligent Systems and Computing 2018, 93 -103.
AMA StyleKatarzyna Poczęta, Łukasz Kubuś, Alexander Yastrebov, Elpiniki I. Papageorgiou. Temperature Forecasting for Energy Saving in Smart Buildings Based on Fuzzy Cognitive Map. Advances in Intelligent Systems and Computing. 2018; ():93-103.
Chicago/Turabian StyleKatarzyna Poczęta; Łukasz Kubuś; Alexander Yastrebov; Elpiniki I. Papageorgiou. 2018. "Temperature Forecasting for Energy Saving in Smart Buildings Based on Fuzzy Cognitive Map." Advances in Intelligent Systems and Computing , no. : 93-103.
Fuzzy cognitive map (FCM) is an effective tool for modeling dynamic decision support systems. It describes the analyzed phenomenon in the form of key concepts and causal connections between them. The main aspect of building of the FCM model is concepts selection. It is usually based on the expert knowledge. The aim of this paper is to introduce a new evolutionary algorithm for fuzzy cognitive maps learning. The proposed approach allows to select key concepts based on graph theory metrics and determine the connections between them. A simulation analysis was done with the use of synthetic and real-life data.
Katarzyna Poczeta; Łukasz Kubuś; Alexander Yastrebov. An Evolutionary Algorithm Based on Graph Theory Metrics for Fuzzy Cognitive Maps Learning. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 137 -149.
AMA StyleKatarzyna Poczeta, Łukasz Kubuś, Alexander Yastrebov. An Evolutionary Algorithm Based on Graph Theory Metrics for Fuzzy Cognitive Maps Learning. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; ():137-149.
Chicago/Turabian StyleKatarzyna Poczeta; Łukasz Kubuś; Alexander Yastrebov. 2017. "An Evolutionary Algorithm Based on Graph Theory Metrics for Fuzzy Cognitive Maps Learning." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 137-149.
Katarzyna Poczęta; Łukasz Kubuś; Alexander Yastrebov; Elpiniki Papageorgiou. Application of Fuzzy Cognitive Maps with Evolutionary Learning Algorithm to Model Decision Support Systems Based on Real-Life and Historical Data. Studies in Computational Intelligence 2017, 717, 153 -175.
AMA StyleKatarzyna Poczęta, Łukasz Kubuś, Alexander Yastrebov, Elpiniki Papageorgiou. Application of Fuzzy Cognitive Maps with Evolutionary Learning Algorithm to Model Decision Support Systems Based on Real-Life and Historical Data. Studies in Computational Intelligence. 2017; 717 ():153-175.
Chicago/Turabian StyleKatarzyna Poczęta; Łukasz Kubuś; Alexander Yastrebov; Elpiniki Papageorgiou. 2017. "Application of Fuzzy Cognitive Maps with Evolutionary Learning Algorithm to Model Decision Support Systems Based on Real-Life and Historical Data." Studies in Computational Intelligence 717, no. : 153-175.
Elpiniki I. Papageorgiou; Katarzyna Poczęta. A two-stage model for time series prediction based on fuzzy cognitive maps and neural networks. Neurocomputing 2017, 232, 113 -121.
AMA StyleElpiniki I. Papageorgiou, Katarzyna Poczęta. A two-stage model for time series prediction based on fuzzy cognitive maps and neural networks. Neurocomputing. 2017; 232 ():113-121.
Chicago/Turabian StyleElpiniki I. Papageorgiou; Katarzyna Poczęta. 2017. "A two-stage model for time series prediction based on fuzzy cognitive maps and neural networks." Neurocomputing 232, no. : 113-121.
Fuzzy cognitive map (FCM) is a soft computing technique for modeling decision support systems. Construction of the FCM model is based on the selection of concepts important for the analyzed problem and determining significant connections between them. Fuzzy cognitive map can be initialized based on expert knowledge or automatic constructed from data with the use of supervised or evolutionary learning algorithm. FCM models learned from data are much denser than those created by experts. This paper proposes a new evolutionary approach for fuzzy cognitive maps learning based on system performance indicators. The learning process has been carried out with the use of Elite Genetic Algorithm and Individually Directional Evolutionary Algorithm. The developed approach allows to receive FCM model more similar to the reference system than standard methods for fuzzy cognitive maps learning.
Katarzyna Poczęta; Łukasz Kubuś; Alexander Yastrebov; Elpiniki Papageorgiou. Learning Fuzzy Cognitive Maps Using Evolutionary Algorithm Based on System Performance Indicators. Advances in Intelligent Systems and Computing 2017, 554 -564.
AMA StyleKatarzyna Poczęta, Łukasz Kubuś, Alexander Yastrebov, Elpiniki Papageorgiou. Learning Fuzzy Cognitive Maps Using Evolutionary Algorithm Based on System Performance Indicators. Advances in Intelligent Systems and Computing. 2017; ():554-564.
Chicago/Turabian StyleKatarzyna Poczęta; Łukasz Kubuś; Alexander Yastrebov; Elpiniki Papageorgiou. 2017. "Learning Fuzzy Cognitive Maps Using Evolutionary Algorithm Based on System Performance Indicators." Advances in Intelligent Systems and Computing , no. : 554-564.
Fuzzy cognitive map (FCM) is a simple and user friendly tool for modeling complex systems. It is described by the set of the concepts and the connections between them. FCM can be initialized based on expert knowledge or automatic constructed with the use of learning algorithms. Most learning methods focus on data error and the structure of the resulting model significantly differs from the reference object. This paper introduces a new multi-objective evolutionary approach for fuzzy cognitive maps learning based on system performance indicators. The proposed solution allows to select the most significant connections in terms of direct and indirect influence between concepts and obtain FCM models more similar to the reference structures. This approach was analyzed with the use of Elite Genetic Algorithm (EGA) and Individually Directional Evolutionary Algorithm (IDEA). Comparative analysis of the proposed methodology with the standard approach of FCMs learning was performed with the use of synthetic and real-life data. The obtained results show that the proposed approach based on system performance indicators outperforms the standard methodology focused only in data error.
Lukasz Kubus; Katarzyna Poczeta; Alexander Yastrebov. A new learning approach for fuzzy cognitive maps based on system performance indicators. 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2016, 1398 -1404.
AMA StyleLukasz Kubus, Katarzyna Poczeta, Alexander Yastrebov. A new learning approach for fuzzy cognitive maps based on system performance indicators. 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2016; ():1398-1404.
Chicago/Turabian StyleLukasz Kubus; Katarzyna Poczeta; Alexander Yastrebov. 2016. "A new learning approach for fuzzy cognitive maps based on system performance indicators." 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) , no. : 1398-1404.
Fuzzy cognitive map (FCM) is a soft computing methodology that allows to describe the analyzed problem as a set of nodes (concepts) and connections (links) between them. In this paper the Structure Optimization Genetic Algorithm (SOGA) for FCMs learning is presented for prediction of indoor temperature. The proposed approach allows to automatically construct and optimize the FCM model on the basis of historical multivariate time series. The SOGA defines a new learning error function with an additional penalty for coping with the high complexity present in an FCM with a large number of concepts and connections between them. The aim of this study is the analysis of usefulness of the Structure Optimization Genetic Algorithm for fuzzy cognitive maps learning on the example of forecasting the indoor temperature of a house. A comparative analysis of the SOGA with other well-known FCM learning algorithms (Real-Coded Genetic Algorithm and Multi-Step Gradient Method) was performed with the use of ISEMK (Intelligent Expert System based on Cognitive Maps) software tool. The obtained results show that the use of SOGA allows to significantly reduce the structure of the FCM model by selecting the most important concepts, connections between them and keeping a high forecasting accuracy.
Katarzyna Poczęta; Alexander Yastrebov; Elpiniki Papageorgiou. Forecasting Indoor Temperature Using Fuzzy Cognitive Maps with Structure Optimization Genetic Algorithm. Artificial Intelligence: Foundations, Theory, and Algorithms 2016, 65 -80.
AMA StyleKatarzyna Poczęta, Alexander Yastrebov, Elpiniki Papageorgiou. Forecasting Indoor Temperature Using Fuzzy Cognitive Maps with Structure Optimization Genetic Algorithm. Artificial Intelligence: Foundations, Theory, and Algorithms. 2016; ():65-80.
Chicago/Turabian StyleKatarzyna Poczęta; Alexander Yastrebov; Elpiniki Papageorgiou. 2016. "Forecasting Indoor Temperature Using Fuzzy Cognitive Maps with Structure Optimization Genetic Algorithm." Artificial Intelligence: Foundations, Theory, and Algorithms , no. : 65-80.
In this study, we propose a new hybrid approach for time series prediction based on the efficient capabilities of fuzzy cognitive maps (FCMs) with structure optimization algorithms and artificial neural networks (ANNs). The proposed structure optimization genetic algorithm (SOGA) for automatic construction of FCM is used for modeling complexity based on historical time series, and artificial neural networks (ANNs) which are used at the final process for making time series prediction. The suggested SOGA-FCM method is used for selecting the most important nodes (attributes) and interconnections among them which in the next stage are used as the input data to ANN used for time series prediction after training. The FCM with efficient learning algorithms and ANN have been already proved as sufficient methods for making time series forecasting. The performance of the proposed approach is presented through the analysis of real data of daily water demand and the corresponding prediction. The multivariate analysis of historical data is held for nine variables, season, month, day or week, holiday, mean and high temperature, rain average, touristic activity and water demand. The whole approach was implemented in an intelligent software tool initially deployed for FCM prediction. Through the experimental analysis, the usefulness of the new hybrid approach in water demand prediction is demonstrated, by calculating the mean absolute error (as one of the well known prediction measures). The results are promising for future work to this direction.
Elpiniki Papageorgiou; Katarzyna Poczęta; Chrysi Laspidou. Hybrid model for water demand prediction based on fuzzy cognitive maps and artificial neural networks. 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 2016, 1523 -1530.
AMA StyleElpiniki Papageorgiou, Katarzyna Poczęta, Chrysi Laspidou. Hybrid model for water demand prediction based on fuzzy cognitive maps and artificial neural networks. 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2016; ():1523-1530.
Chicago/Turabian StyleElpiniki Papageorgiou; Katarzyna Poczęta; Chrysi Laspidou. 2016. "Hybrid model for water demand prediction based on fuzzy cognitive maps and artificial neural networks." 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) , no. : 1523-1530.
Konstantinos Kokkinos; Elpiniki I. Papageorgiou; Katarzyna Poczeta; Lefteris Papadopoulos; Chrysi Laspidou. Soft Computing Approaches for Urban Water Demand Forecasting. Intelligent Decision Technologies 2017 2016, 57, 357 -367.
AMA StyleKonstantinos Kokkinos, Elpiniki I. Papageorgiou, Katarzyna Poczeta, Lefteris Papadopoulos, Chrysi Laspidou. Soft Computing Approaches for Urban Water Demand Forecasting. Intelligent Decision Technologies 2017. 2016; 57 ():357-367.
Chicago/Turabian StyleKonstantinos Kokkinos; Elpiniki I. Papageorgiou; Katarzyna Poczeta; Lefteris Papadopoulos; Chrysi Laspidou. 2016. "Soft Computing Approaches for Urban Water Demand Forecasting." Intelligent Decision Technologies 2017 57, no. : 357-367.
Katarzyna Poczeta; Alexander Yastrebov; Elpiniki Papageorgiou. Learning Fuzzy Cognitive Maps using Structure Optimization Genetic Algorithm. 2015 Federated Conference on Computer Science and Information Systems 2015, 1 .
AMA StyleKatarzyna Poczeta, Alexander Yastrebov, Elpiniki Papageorgiou. Learning Fuzzy Cognitive Maps using Structure Optimization Genetic Algorithm. 2015 Federated Conference on Computer Science and Information Systems. 2015; ():1.
Chicago/Turabian StyleKatarzyna Poczeta; Alexander Yastrebov; Elpiniki Papageorgiou. 2015. "Learning Fuzzy Cognitive Maps using Structure Optimization Genetic Algorithm." 2015 Federated Conference on Computer Science and Information Systems , no. : 1.
The purposes of this research are to find a model to forecast the electricity consumption in a household based on fuzzy cognitive map (FCM) prediction capabilities. The data analysis has been performed with three different learning algorithms based on the fuzzy cognitive map model which are (a) the multi-step gradient method (MGM), (b) the real coded genetic algorithm (RCGA) and (c) the structure optimization genetic algorithm (SOGA) as a new improvement of the RCGA able to handle the model's complexity investigated in our study. The suitable forecasting methods and two different forecasting periods were chosen by considering the smallest value of MSE (Mean Square Error) and RMSE (Root Mean Square Error), respectively. The proposed FCM-based prediction algorithms were compared with known statistical methods such as ARIMA and artificial intelligent methods such as ANNs and neuro-fuzzy models. The experiments conducted in this study showed that the multi gradient-based algorithm for FCM learning was the most efficient with respect to the accuracy of prediction expressed by the well-known used errors in the two selected forecasting periods.
Elpiniki I. Papageorgiou; Katarzyna Poczeta. Application of fuzzy cognitive maps to electricity consumption prediction. 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC) 2015, 1 -6.
AMA StyleElpiniki I. Papageorgiou, Katarzyna Poczeta. Application of fuzzy cognitive maps to electricity consumption prediction. 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC). 2015; ():1-6.
Chicago/Turabian StyleElpiniki I. Papageorgiou; Katarzyna Poczeta. 2015. "Application of fuzzy cognitive maps to electricity consumption prediction." 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC) , no. : 1-6.
Elpiniki I. Papageorgiou; Katarzyna Poczęta; Alexander Yastrebov; Chrysi Laspidou. Fuzzy Cognitive Maps and Multi-step Gradient Methods for Prediction: Applications to Electricity Consumption and Stock Exchange Returns. Blockchain Technology and Innovations in Business Processes 2015, 501 -511.
AMA StyleElpiniki I. Papageorgiou, Katarzyna Poczęta, Alexander Yastrebov, Chrysi Laspidou. Fuzzy Cognitive Maps and Multi-step Gradient Methods for Prediction: Applications to Electricity Consumption and Stock Exchange Returns. Blockchain Technology and Innovations in Business Processes. 2015; ():501-511.
Chicago/Turabian StyleElpiniki I. Papageorgiou; Katarzyna Poczęta; Alexander Yastrebov; Chrysi Laspidou. 2015. "Fuzzy Cognitive Maps and Multi-step Gradient Methods for Prediction: Applications to Electricity Consumption and Stock Exchange Returns." Blockchain Technology and Innovations in Business Processes , no. : 501-511.