Parameter analysis for sigmoid and hyperbolic transfer functions of fuzzy cognitive maps View Full Text


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Article Info

DATE

2022-05-29

AUTHORS

Themistoklis Koutsellis, Georgios Xexakis, Konstantinos Koasidis, Alexandros Nikas, Haris Doukas

ABSTRACT

Fuzzy cognitive maps (FCM) have recently gained ground in many engineering applications, mainly because they allow stakeholder engagement in reduced-form complex systems representation and modelling. They provide a pictorial form of systems, consisting of nodes (concepts) and node interconnections (weights), and perform system simulations for various input combinations. Due to their simplicity and quasi-quantitative nature, they can be easily used with and by non-experts. However, these features come with the price of ambiguity in output: recent literature indicates that changes in selected FCM parameters yield considerably different outcomes. Furthermore, it is not a priori known whether an FCM simulation would reach a fixed, unique final state (fixed point). There are cases where infinite, chaotic, or cyclic behaviour (non-convergence) hinders the inference process, and literature shows that the primary culprit lies in a parameter determining the steepness of the most common transfer functions, which determine the state vector of the system during FCM simulations. To address ambiguity in FCM outcomes, we propose a certain range for the value of this parameter, λ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\uplambda }$$\end{document}, which is dependent on the FCM layout, for the case of the log-sigmoid and hyperbolic tangent transfer functions. The analysis of this paper is illustrated through a novel software application, In-Cognitive, which allows non-experts to define the FCM layout via a Graphical User Interface and then perform FCM simulations given various inputs. The proposed methodology and developed software are validated against a real-world energy policy-related problem in Greece, drawn from the literature. More... »

PAGES

5733-5763

References to SciGraph publications

  • 2020-07-30. Making Predictions of Global Warming Impacts Using a Semantic Web Tool that Simulates Fuzzy Cognitive Maps in COMPUTATIONAL ECONOMICS
  • 2004-05. Investment analysis & decision making in markets using adaptive fuzzy causal relationships in OPERATIONAL RESEARCH
  • 2020-09-23. A fuzzy cognitive map based on Nash bargaining game for supplier selection problem: a case study on auto parts industry in OPERATIONAL RESEARCH
  • 2017-08-05. Fuzzy Cognitive Maps Based Models for Pattern Classification: Advances and Challenges in SOFT COMPUTING BASED OPTIMIZATION AND DECISION MODELS
  • 2018-05-18. On the Existence and Uniqueness of Fixed Points of Fuzzy Cognitive Maps in INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS. THEORY AND FOUNDATIONS
  • 2019-04-29. Assessing the operational and economic efficiency benefits of dynamic manufacturing networks through fuzzy cognitive maps: a case study in OPERATIONAL RESEARCH
  • 2016-07-13. Developing Robust Climate Policies: A Fuzzy Cognitive Map Approach in ROBUSTNESS ANALYSIS IN DECISION AIDING, OPTIMIZATION, AND ANALYTICS
  • 2017-08-17. A review on methods and software for fuzzy cognitive maps in ARTIFICIAL INTELLIGENCE REVIEW
  • 2013-12-03. JFCM : A Java Library for FuzzyCognitive Maps in FUZZY COGNITIVE MAPS FOR APPLIED SCIENCES AND ENGINEERING
  • 2017-08-17. A Medical Decision Support System for the Prediction of the Coronary Artery Disease Using Fuzzy Cognitive Maps in CREATIVITY IN INTELLIGENT TECHNOLOGIES AND DATA SCIENCE
  • 2005. Using Fuzzy Cognitive Maps as a Decision Support System for Political Decisions: The Case of Turkey’s Integration into the European Union in ADVANCES IN INFORMATICS
  • 2017-07-11. Analyzing the dynamics behind ethical banking practices using fuzzy cognitive mapping in OPERATIONAL RESEARCH
  • 2010. A Generic Tool for Building Fuzzy Cognitive Map Systems in ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS
  • 2010. Fuzzy Cognitive Networks: Adaptive Network Estimation and Control Paradigms in FUZZY COGNITIVE MAPS
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