Abstract
A fuzzy cognitive maps (FCM) is a cognitive map within the relations between the elements. FCM has been widely used in many applications such as experts system and knowledge engineering. However, classical FCM is inherently short of sufficient capability of representing and aggregating uncertain information. In this paper, generalized FCM (GFCM) is proposed based on genetic algorithm and interval numbers. An application frame of GFCM is detailed. At last, a numerical example about socio-economic system is used to illustrate the effectiveness of the proposed methodology.
Highlights
fuzzy cognitive maps (FCM) has received special attentions from the scientific community and done many achievements since it can provide a powerful tool to manipulate knowledge imitating human reasoning and thinking
FCM has used to solve many problems like fuzzy control (Stylios and Groumpos 1999), approximate reasoning (Khan and Quaddus 2004), strategic planning (Konar and Chakraborty 2005), data mining analysis (Yang and Peng 2009), virtual worlds and network models (Dickerson and Kosko 1993), and so on (Gupta and Gandhi 2013, 2014; Kandasamy and Indra 2000; Jorge et al 2011; Yesil et al 2013; Ganguli 2014; Papageorgiou and Iakovidis 2013; Salmeron and Papageorgiou 2014; Glykas 2013; Napoles et al 2013; Gray et al 2014; Stylios and Groumpos 2000). It is noted in the real application that Papageorgiou (2011) presents a novel framework for the construction of augmented FCMs based on fuzzy rule-extraction methods for decisions in medical informatics
Results of generalized FCM (GFCM) simulations (Trend of lower bound of each concept value)
Summary
FCM has received special attentions from the scientific community and done many achievements since it can provide a powerful tool to manipulate knowledge imitating human reasoning and thinking. FCM has used to solve many problems like fuzzy control (Stylios and Groumpos 1999), approximate reasoning (Khan and Quaddus 2004), strategic planning (Konar and Chakraborty 2005), data mining analysis (Yang and Peng 2009), virtual worlds and network models (Dickerson and Kosko 1993), and so on (Gupta and Gandhi 2013, 2014; Kandasamy and Indra 2000; Jorge et al 2011; Yesil et al 2013; Ganguli 2014; Papageorgiou and Iakovidis 2013; Salmeron and Papageorgiou 2014; Glykas 2013; Napoles et al 2013; Gray et al 2014; Stylios and Groumpos 2000) It is noted in the real application that Papageorgiou (2011) presents a novel framework for the construction of augmented FCMs based on fuzzy rule-extraction methods for decisions in medical informatics.
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