Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links

• Published in: International journal of human-computer studies Vol. 64; no. 8; pp. 727 - 743
• Main Authors: Papageorgiou, Elpiniki I., Stylios, Chrysostomos, Groumpos, Peter P.
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.08.2006; Elsevier
• Subjects: Applied sciences; Computer assisted instruction; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Control theory. Systems; Education; Educational technology; Exact sciences and technology; Fuzzy cognitive maps; Fuzzy set theory; Hebbian learning; Learning algorithms; Process control. Computer integrated manufacturing; Process modeling and control; Software; Students; Teaching; Learning algorithms; Hebbian learning; Process modeling and control; Fuzzy cognitive maps; Industrial control; Process control; Knowledge base; Neural network; Modeling; Fuzzy neural nets; Unsupervised learning; Human factor; Logic circuit; Fuzzy logic; Cognitive map; Educational software program; Behavioral analysis; User interface; Causality; Learning algorithm; Artificial intelligence; Directed graph
• ISSN: 1071-5819; 1095-9300
• DOI: 10.1016/j.ijhcs.2006.02.009

Fuzzy Cognitive Maps (FCMs) constitute an attractive knowledge-based methodology, combining the robust properties of fuzzy logic and neural networks. FCMs represent causal knowledge as a signed directed graph with feedback and provide an intuitive framework which incorporates the experts’ knowledge. FCMs handle available information and knowledge from an abstract point of view. They develop behavioural model of the system exploiting the experience and knowledge of experts. The construction of FCMs is based mainly on experts who determine the structure of FCM, i.e. concepts and weighted interconnections among concepts. But this methodology may not be a sufficient model of the system because the human factor is not always reliable. Thus the FCM model of the system may requires restructuring which is achieved through adjustment the weights of FCM interconnections using specific learning algorithms for FCMs. In this article, two unsupervised learning algorithms are presented and compared for training FCMs; how they define, select or fine-tuning weights of the causal interconnections among concepts. The implementation and results of these unsupervised learning techniques for an industrial process control problem are discussed. The simulations results of training the process system verify the effectiveness, validity and advantageous characteristics of those learning techniques for FCMs.