Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder

Authors:
Arthi Kannappan;A. Tamilarasi;E. I. Papageorgiou
Affiliations:
MCA Department, Karpagam College of Engineering, Coimbatore 641 032, Tamil Nadu, India;MCA Department, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India;Department of Informatics and Computer Technology, Technological Educational Institute of Lamia, 3rd km PEO Lamia-Athens, 35100 Lamia, Greece
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 11
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Abstract

The soft computing technique of fuzzy cognitive maps (FCM) for modeling and predicting autistic spectrum disorder has been proposed. The FCM models the behavior of a complex system and is used to develop new knowledge based system applications. FCM combines the robust properties of fuzzy logic and neural networks. To overwhelm the limitations and to improve the efficiency of FCM, a good learning method of unsupervised training could be applied. A decision system based on human knowledge and experience with a FCM trained using unsupervised non-linear hebbian learning algorithm is proposed here. Through this work the hebbian algorithm on non-linear units is used for training FCMs for the autistic disorder prediction problem. The investigated approach serves as a guide in determining the prognosis and in planning the appropriate therapies to special children.