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

N. Martin and R. Priya

  DOI:
  https://doi.org/10.37418/amsj.9.4.66

Full Text

Fuzzy cognitive map is a tool which establishes the causal relation between the factors of decision-making problem. It is very instrumental in modeling complex system with expert’s opinion, but the confinement of the expert’s opinion to crisp values makes the scenario unrealistic. To overcome such conflicts of pragmatic representations, intuitionistic fuzzy cognitive maps with membership and non-membership association weights of the factors are used. In this research work a new approach of non-linear Hebbian learning algorithm to handle intuitionistic fuzzy cognitive maps is initiated, based on this approach anxiety predictive model is proposed. In the field of psychoanalysis, the word anxiety occupies a prime position as many researchers find that the rate of people getting affected by this disorder is increasing due to various peripheral and intramural factors. This work aims in finding the factors contributing to anxiety disorder and this modeling approach will certainly assist in planning and administering appropriate treatment to the victim.


Keywords:
Intuitionistic Fuzzy cognitive Maps, Non-Linear Hebbian learning algorithm, Anxiety disorder.