A flexible nonlinear approach to represent cause-effect relationships in FCMs
Fuzzy Cognitive Maps is an important representation tool that has the ability to model complicated systems. Based on linear influence relations between concepts, FCMs can be trained to lead a system to a desired state. This work proposes a generalized flexible nonlinear function as an alternative way of estimating that influence. Its adjustable character offers the ability to lead a FCM into a large set of different equilibrium states, where the conventional approach constitutes only one instance. Experimental studies present the properties of the proposed methodology in two benchmarks and other synthetic data. The examination of a system under different considerations of influence offers a more complete understanding of a system behavior. (C) 2012 Elsevier B. V. All rights reserved.
fuzzy cognitive maps influence nonlinear function generalized logistic function