NON-LINEAR HEBBIAN LEARNING ALGORITHM FOR INTUITIONISTIC FUZZY COGNITIVE MAPS IN PREDICTIVE MODEL

2020 ◽  
Vol 9 (4) ◽  
pp. 2075-2081
Author(s):  
N. Martin ◽  
R. Priya
Keyword(s):  
Predictive Model ◽  
Hebbian Learning ◽  
Learning Algorithm ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Intuitionistic Fuzzy ◽  
Non Linear

scholarly journals Active Hebbian learning algorithm to train fuzzy cognitive maps

2004 ◽  
Vol 37 (3) ◽  
pp. 219-249 ◽  
Author(s):  
E.I. Papageorgiou ◽  
C.D. Stylios ◽  
P.P. Groumpos
Keyword(s):  
Hebbian Learning ◽  
Learning Algorithm ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps

Modeling of Parkinson's Disease Using Fuzzy Cognitive Maps and Non-Linear Hebbian Learning

2014 ◽  
Vol 23 (05) ◽  
pp. 1450010 ◽  
Author(s):  
Antigoni P. Anninou ◽  
Peter P. Groumpos
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Hebbian Learning ◽  
Neurodegenerative Disorder ◽  
Large Population ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Medical Problems ◽  
Age Related ◽  
Non Linear

Parkinson's disease is a chronic, progressive, age-related, neurodegenerative disorder that affects a large population around the world. A mathematical model for Parkinson's disease is presented using Fuzzy Cognitive Maps (FCMs). Basic theories of FCMs are reviewed and presented. Decision Support Systems (DSS) for medical problems are reviewed. Non-linear Hebbian learning techniques are considered in studying Medical problems and a generic algorithm is presented. The proposed method used the knowledge of a number of experts and simulations were performed obtaining interesting results. Comparisons of the results of the proposed method, both by making use and not making use of learning algorithms, are presented. Some interesting future research directions are mentioned.

A Methodology for Developing Adaptive Fuzzy Cognitive Maps for Decision Support

2000 ◽  
Vol 4 (6) ◽  
pp. 403-407 ◽  
Author(s):  
M. Shamim Khan ◽  
◽  
Alex Chong ◽  
Tom Gedeon
Keyword(s):  
Decision Support ◽  
Dynamic System ◽  
Hebbian Learning ◽  
Real Life ◽  
Cognitive Maps ◽  
Causal Link ◽  
Fuzzy Cognitive Maps ◽  
Event Sequence ◽  
Event Sequences ◽  
Sequence Encoding

Differential Hebbian Learning (DHL) was proposed by Kosko as an unsupervised learning scheme for Fuzzy Cognitive Maps (FCMs). DHL can be used with a sequence of state vectors to adapt the causal link strengths of an FCM. However, it does not guarantee learning of the sequence by the FCM and no concrete procedures for the use of DHL has been developed. In this paper a formal methodology is proposed for using DHL in the development of FCMs in a decision support context. The four steps in the methodology are: (1) Creation of a crisp cognitive map; (2) Identification of event sequences for use in DHL; (3) Event sequence encoding using DHL; (4) Revision of the trained FCM. Feasibility of the proposed methodology is demonstrated with an example involving a dynamic system with feedback based on a real-life scenario.

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