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Fuzzy Cognitive Maps

Advances in Theory, Methodologies, Tools and Applications

  • Book
  • © 2010

Overview

Editors:
  1. Michael Glykas

    1. University of Aegean, Chios, Greece

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  • Contains the theory of fuzzy cognitive mapping and for the first time a methodology of constructing and linking fuzzy cognitive maps to be used in management and business process reengineering for performance measurement purposes
  • Contains a software tool that can be used for designing and simulating FCMs that can be linked and simulated as an integrated project
  • Written by experts in the field

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 247)

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Table of contents (17 chapters)

  1. Front Matter

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  2. Fuzzy Cognitive Maps: Basic Theories and Their Application to Complex Systems

    • Peter P. Groumpos
    Pages 1-22

  3. Expert-Based and Computational Methods for Developing Fuzzy Cognitive Maps

    • Wojciech Stach, Lukasz Kurgan, Witold Pedrycz
    Pages 23-41

  4. A Novel Approach on Constructed Dynamic Fuzzy Cognitive Maps Using Fuzzified Decision Trees and Knowledge-Extraction Techniques

    • Elpiniki I. Papageorgiou
    Pages 43-70

  5. The FCM Designer Tool

    • Aguilar Jose, José Contreras
    Pages 71-87

  6. Fuzzy Cognitive Networks: Adaptive Network Estimation and Control Paradigms

    • Thodoris L. Kottas, Yiannis S. Boutalis, Manoli A. Christodoulou
    Pages 89-134

  7. Modeling of Operative Risk Using Fuzzy Expert Systems

    • Santiago Medina Hurtado
    Pages 135-159

  8. Fuzzy Cognitive Maps in Banking Business Process Performance Measurement

    • George Xirogiannis, Michael Glykas, Christos Staikouras
    Pages 161-200

  9. Fuzzy Cognitive Maps-Based IT Projects Risks Scenarios

    • Jose L. Salmeron
    Pages 201-215

  10. Software Reliability Modelling Using Fuzzy Cognitive Maps

    • Panagiotis Chytas, Michael Glykas, George Valiris
    Pages 217-230

  11. Fuzzy Cognitive Networks for Maximum Power Point Tracking in Photovoltaic Arrays

    • Thodoris L. Kottas, Athanasios D. Karlis, Yiannis S. Boutalis
    Pages 231-257

  12. Fuzzy Cognitive Maps Applied to Computer Vision Tasks

    • Gonzalo Pajares, María Guijarro, P. Javier Herrera, José J. Ruz, Jesús M. de la Cruz
    Pages 259-289

  13. Classifying Patterns Using Fuzzy Cognitive Maps

    • G. A. Papakostas, D. E. Koulouriotis
    Pages 291-306

  14. Dynamic Fuzzy Cognitive Maps for the Supervision of Multiagent Systems

    • Aguilar Jose
    Pages 307-324

  15. Soft Computing Technique of Fuzzy Cognitive Maps to Connect Yield Defining Parameters with Yield in Cotton Crop Production in Central Greece as a Basis for a Decision Support System for Precision Agriculture Application

    • E. I. Papageorgiou, A. T. Markinos, T. A. Gemtos
    Pages 325-362

  16. Analysis of Farmers’ Concepts of Environmental Management Measures: An Application of Cognitive Maps and Cluster Analysis in Pursuit of Modelling Agents’ Behaviour

    • Livia Ortolani, Neil McRoberts, Nicholas Dendoncker, Mark Rounsevell
    Pages 363-381

  17. Using Fuzzy Cognitive Maps to Support the Analysis of Stakeholders’ Views of Water Resource Use and Water Quality Policy

    • Alkis Kafetzis, Neil McRoberts, Ioanna Mouratiadou
    Pages 383-402

  18. Fuzzy Cognitive Map to Support Conflict Analysis in Drought Management

    • R. Giordano, M. Vurro
    Pages 403-425

  19. Back Matter

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Keywords

About this book

This important edited volume is the first such book ever published on fuzzy cognitive maps (FCMs). Professor Michael Glykas has done an exceptional job in bringing together and editing its seventeen chapters. The volume appears nearly a quarter century after my original article “Fuzzy Cognitive Maps” appeared in the International Journal of Man-Machine Studies in 1986. The volume accordingly reflects many years of research effort in the development of FCM theory and applications—and portends many more decades of FCM research and applications to come. FCMs are fuzzy feedback models of causality. They combine aspects of fuzzy logic, neural networks, semantic networks, expert systems, and nonlinear dynamical systems. That rich structure endows FCMs with their own complexity and lets them apply to a wide range of problems in engineering and in the soft and hard sciences. Their partial edge connections allow a user to directly represent causality as a matter of degree and to learn new edge strengths from training data. Their directed graph structure allows forward or what-if inferencing. FCM cycles or feedback paths allow for complex nonlinear dynamics. Control of FCM nonlinear dynamics can in many cases let the user encode and decode concept patterns as fixed-point attractors or limit cycles or perhaps as more exotic dynamical equilibria. These global equilibrium patterns are often “hidden” in the nonlinear dynamics. The user will not likely see these global patterns by simply inspecting the local causal edges or nodes of large FCMs.

Editors and Affiliations

  • University of Aegean, Chios, Greece

    Michael Glykas

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