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检索条件"主题词=Structure Optimization Genetic Algorithm"

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Learning Fuzzy Cognitive Maps using structure optimization genetic algorithm

Learning Fuzzy Cognitive Maps using Structure Optimization G...

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3rd International Conference on Innovative Network Systems and Applications (iNetSApp) held in conjunction with Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Poczeta, Katarzyna Yastrebov, Alexander Papageorgiou, Elpiniki I. Kielce Univ Technol Al Tysiaclecia Panstwa Polskiego 7 PL-25314 Kielce Poland CERTH Ctr Res & Technol Hellas Thermi 57001 Greece Technol Educ Inst TEI Cent Greece Lamia 35100 Greece

ISBN: (纸本)9788360810668

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 a new structure optimization genetic algorithm (SOGA) for FCMs learning is presented for modeling complex decision support systems. The proposed approach allows to automatic 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 highly complexity of FCM understood as a large number of concepts and a large number of connections between them. The aim of this study is the analysis of usefulness of the structure optimization genetic algorithm for fuzzy cognitive maps learning. Comparative analysis of the SOGA with other well-known FCM learning algorithms (Real-Coded genetic algorithm and Multi-Step Gradient Method) was performed on the example of prediction of rented bikes count. Simulations were done with the 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 and connections between them.

关键词： Fuzzy Cognitive Maps structure optimization genetic algorithm Real-Coded genetic algorithm Multi-Step Gradient Method

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Application of Fuzzy Cognitive Maps to water demand prediction

Application of Fuzzy Cognitive Maps to water demand predicti...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Papageorgiou, Elpiniki I. Poczeta, Katarzyna Laspidou, Chrysi CERTH Ctr Res & Technol Hellas Inst Informat Technol 6th Km Charilaou Thermi Rd Thermi 57001 Greece Technol Educ Inst TEI Cent Greece Dept Comp Engn Lamia 35100 Greece Kielce Univ Technol Dept Comp Sci Applicat Kielce Poland Univ Thessaly Dept Civil Engn Volos 38334 Volos Greece

ISBN: (纸本)9781467374286

This article is focused on the issue of learning of Fuzzy Cognitive Maps designed to model and predict time series. The multi-step supervised-learning based-on-gradient methods as well as population-based learning, with the use of real coded genetic algorithms, are described. In this study, a new structure optimization genetic algorithm for fuzzy cognitive maps learning is proposed for automatic construction of FCM applied to time series prediction. The proposed learning methodologies are based on an FCM reconstruction procedure using historical time series. The main contribution of this study is the analysis of the use of FCMs with their learning algorithms based on the multi-step gradient method (MGM) and other population-based methods to predict water demand. The performance of learning algorithms is presented through the analysis of real data of daily water demand and the corresponding prediction. The multivariate analysis of historical water demand data is held for five variables, mean and high temperature, precipitation, wind speed and touristic activity. Simulation results were obtained with the ISEMK (Intelligent Expert System based on Cognitive Maps) software tool. Through the experimental analysis, we demonstrate the usefulness of the new proposed FCM learning algorithm in water demand prediction, by calculating the known prediction errors. The advantage of the optimization genetic algorithm structure is its ability to select the most significant relations between concepts for prediction.

关键词： Fuzzy Cognitive Maps Multi-Step Learning algorithms Population-Based Learning algorithms Real Coded genetic algorithm structure optimization genetic algorithm Gradient Method

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