Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/10040
Title: A framework for static and dynamic analysis of multi-layer fuzzy cognitive maps
Authors: Christoforou, Andreas 
Andreou, Andreas S. 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Cycles;Multi-layer fuzzy cognitive maps;Static and dynamic analysis;Simulations;Node strength
Issue Date: 5-Apr-2017
Source: Neurocomputing, 2017, vol. 232, pp. 133-145
Volume: 232
Start page: 133
End page: 145
Journal: Neurocomputing 
Abstract: Fuzzy Cognitive Maps (FCMs) have progressively become a well-researched and extensively used set of tools for modeling real-world, complex decision making problems. Despite their fast growth, researchers and modelers are faced with the lack of a framework to analyze such models and help them assess their performance and efficiency. Moreover, when multi-layered FCM (ML-FCM) structures are used, which consist of a rich number of nodes and interconnections organized in different layers, this need becomes imperative. The present paper introduces an integrated analysis framework and a series of steps to gather useful static and dynamic information regarding ML-FCM models, as well as to interpret the corresponding results. The proposed type of analysis provides significant information on the model's complexity, the strength of its nodes and its tendency to promote or inhibit activation levels as a result of the presence of positive or negative cycles. In addition, it offers the means to perform dynamic analysis in the form of what-if scenarios. The framework is described and assessed using real-world problems from the engineering and political decision-making domains, which demonstrate its power and usefulness.
URI: https://hdl.handle.net/20.500.14279/10040
ISSN: 09252312
DOI: 10.1016/j.neucom.2016.09.115
Rights: © Elsevier
Type: Article
Affiliation : University of Patras 
Cyprus University of Technology 
Appears in Collections:Άρθρα/Articles

CORE Recommender
Show full item record

SCOPUSTM   
Citations

35
checked on Nov 9, 2023

WEB OF SCIENCETM
Citations 20

27
Last Week
0
Last month
0
checked on Oct 29, 2023

Page view(s)

409
Last Week
3
Last month
10
checked on Jun 23, 2024

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.