Using Fuzzy Grey Cognitive Maps for Industrial Processes Control
Recently, Fuzzy Grey Cognitive Maps (FGCM) has been proposed as a Grey System theory-based FCM extension. Grey systems have become a very effective theory for solving problems within environments with high uncertainty, under discrete small and incomplete
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Using Fuzzy Grey Cognitive Maps for Industrial Processes Control Jose L. Salmeron and Elpiniki I. Papageorgiou
Abstract Recently, Fuzzy Grey Cognitive Maps (FGCM) has been proposed as a Grey System theory-based FCM extension. Grey systems have become a very effective theory for solving problems within environments with high uncertainty, under discrete small and incomplete data sets. The benefits of FGCMs over conventional FCMs make evident the significance of developing a greyness-based cognitive model such as FGCM. In this chapter, the FGCM model and the proposed NHL learning algorithm were applied within an industrial problem, concerning a chemical process control process with two tanks, three valves, one heating element and two thermometers for each tank. The proposed mathematical formulation of FGCMs and the implementation of the NHL algorithm have been successfully applied. This type of learning rule accompanied with the good knowledge of the given system, guarantee the successful implementation of the proposed technique in industrial process control problems.
1 Introduction Fuzzy Cognitive Maps (FCMs) constitute neuro-fuzzy systems, which are able to model complex systems [5, 6]. Recently, Fuzzy Grey Cognitive Maps (FGCM) has been proposed as a FCM extension [15]. It is an innovative and flexible model based Electronic supplementary material The online version of this article (doi: 10.1007/978-3642-39739-4_14) contains supplementary material, which is available to authorized users. J. L. Salmeron (B) Computational Intelligence Lab, University Pablo de Olavide, 1st km. Utrera Road, Seville, Spain e-mail: [email protected] E. I. Papageorgiou Department of Computer Engineering, Technological Educational Institute of Central Greece, 3rd Km Old National Road Lamia-Athens, 35100 Lamia, Greece e-mail: [email protected] E. I. Papageorgiou (ed.), Fuzzy Cognitive Maps for Applied Sciences and Engineering, Intelligent Systems Reference Library 54, DOI: 10.1007/978-3-642-39739-4_14, © Springer-Verlag Berlin Heidelberg 2014
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J. L. Salmeron and E. I. Papageorgiou
on Grey Systems Theory and Fuzzy Cognitive Maps. FGCM is based on GST, that it has become a very worthy theory for solving problems within domains with high uncertainty, under discrete small and incomplete data sets [16, 18, 19]. FGCMs offer several advantages in comparison with others similar techniques. First, the FGCM model is designed specifically for multiple meanings (grey) environments. Second, FGCM allows the defining of relationships between concepts. Through this characteristic, more reliable decisional models for interrelated environments are defined. Third, the FGCM technique is able to quantify the grey influence of the relationships between concepts. Through this attribute, a better support in grey environments can be reached. Finally, with this FGCM model it is possible to develop a what-if analysis with the purpose of describing possible grey scenarios. IT projects risks are modelled to illustrate the proposed technique. Furtherm
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