Intelligent Systems Reference Library

Volume 54

Series Editors

J. Kacprzyk, Warsaw, PolandL. C. Jain, Canberra, Australia

For further volumes:http://www.springer.com/series/8578

Elpiniki I. PapageorgiouEditor

Fuzzy Cognitive Mapsfor Applied Sciencesand Engineering

From Fundamentals to Extensionsand Learning Algorithms

123

EditorElpiniki I. PapageorgiouDepartment of Computer EngineeringTechnological Educational Institute of Central GreeceLamiaGreece

ISSN 1868-4394 ISSN 1868-4408 (electronic)ISBN 978-3-642-39738-7 ISBN 978-3-642-39739-4 (eBook)DOI 10.1007/978-3-642-39739-4Springer Heidelberg New York Dordrecht London

Library of Congress Control Number: 2013950727

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To my husband Nikos for his patience allthese years

and

To Yiannis and Alexandros, the two sunsof my life

Foreword

Prof. Elipiniki I. Papageorgiou has organized and edited an important new con-tribution to the rapidly growing field of fuzzy cognitive maps (FCMs). This newvolume further extends the practical and theoretical boundaries of this interna-tional FCM research effort. It includes several predictive FCM models that rangefrom rulebased systems to adaptive dynamical systems with their own learninglaws.

These diverse models suggest that FCMs will play important roles in the futureof both computing and machine intelligence. This includes applications to so-called ‘‘big data’’ and what we can here call ‘‘big knowledge.’’

FCMs are natural tools to process big data. Their graphical structure of a cyclicsigned directed graph allows the user to specify coarse or fine levels of causalgranularity through the choice of concept nodes and causal edges. Controlling thecausal granularity can combat the systemic problem of exponential rule explosionor the curse of dimensionality that infests large rule-based systems and semanticnetworks. Controlled FCM granularity results in a coarse or fine rule-basedcompression of the streaming data because the directed causal links define fuzzyif–then rules. Granularized FCMs can use statistical learning laws that scale withthe data stream itself. Such adaptive FCMs can gradually update the causal linksand can form or split or delete concept nodes as the data streams into the system(although the FCM field still needs a good data-based theory of concept-nodeformation). This learning process can go on indefinitely. Users can also add ordelete FCM elements at any time in the learning process. A user or higher leveladaptive system can adjust the learning-rate parameters to match the flow of thedata. The resulting FCM at any given time always gives high-level causal andpredictive insight into the data flux.

FCMs also offer a natural representational framework for what we can call bigknowledge—the world’s vast and growing body of expert analysis and advice.

This structured knowledge often takes the traditional form of books or technicalarticles or essays. But it can also take the form of Internet blogs or media inter-views or expert-witness court transcripts. Big knowledge includes the whole

vii

panoply of fixed or oral knowledge that the law calls documentary or testimonialevidence. Today that knowledge exists largely in disconnected sources or chunksaround the world. FCMs have the ability to connect and combine these knowledgechunks into a unified framework for policy and engineering analysis and for fur-ther computer and knowledge processing.

FCMs can synthesize this disparate knowledge through a simple transformtechnique involving connection or adjacency matrices. The technique resembleshow a Fourier transform converts a time signal into the frequency domain wherethe user can filter or modify the signal and then inverse-transform the result backto the time domain. An FCM can represent each knowledge chunk or expertcontribution. Then we can translate each such FCM knowledge chunk into anaugmented square connection matrix conformable for addition. That in turn allowsthe formation of a massive knowledge base by appropriately weighting andcombining the matrices into a large sparse matrix. Then translating the matrix backinto an FCM causal digraph gives the final knowledge base as a massive FCM.

This fusion of all structured knowledge amounts to a worldwide FCMknowledge-representation project. This long-term effort will be the direct benefi-ciary of Google Books and the Gutenberg Project and Wikisource and relatedlarge-scale efforts to digitize and make available the world’s text-based docu-mentary evidence. Every book chapter or essay should have its own FCMinstantiation. So far there have been a few manual efforts at such FCM knowledgetranslation and synthesis. That includes some of the FCMs developed in thisvolume. But a fully automated FCM synthesizer remains a research goal for thefuture.

FCMs can advance big knowledge in yet another way: they can naturallyrepresent deep knowledge in stacked or multilayered FCMs. These multilayerstructures are far more complex and expressive than stacked or deep neural net-works. Almost all such multilayered neural networks have only a feedforwardarchitecture and thus they have only trivial dynamics. They have no connections atall among the neurons in a given visible layer or hidden layer. Nor do the synapticedges or most neurons have any meaningful interpretation. So these minimalmultilayer structures allow little more than blind statistical training of the con-tiguous layers and of the overall network itself. But an FCM’s representationpower and rich feedback dynamics stem directly from the cyclic causal edgeconnections among the concept nodes in a layer—cycles that undermine mosttraditional expert systems and Bayesian networks. Stacked FCMs can representknowledge on different timescales both within FCM layers and especially betweenFCM layers. These stacked FCMs also do not need to function only in feedforwardmode. They can allow feedback from higher layers to lower layers and do so againon different timescales. Thus the entire stacked or multilayer FCM can reverberateas it passes through successive dynamical equilibria. Concept nodes in a given

viii Foreword

FCM layer can also branch laterally to other FCMs or even to other stacked FCMs.Hence they too can fuse or combine with other FCMs to produce ever largerconnected knowledge bases.

These are near-term and long-term goals for FCM research. The present volumedoes an excellent job of moving in those and other directions as well as demon-strating the analytic and predictive power of FCM-based knowledge engineering.

Bart KoskoProfessor of Electrical Engineering and Law

University of Southern CaliforniaLos Angeles

USA

Foreword ix

Contents

1 Methods and Algorithms for Fuzzy CognitiveMap-based Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Elpiniki I. Papageorgiou and Jose L. Salmeron

2 Fuzzy Cognitive Maps as Representations of Mental Modelsand Group Beliefs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29S. A. Gray, E. Zanre and S. R. J. Gray

3 FCM Relationship Modeling for Engineering Systems . . . . . . . . . . 49O. Motlagh, S. H. Tang, F. A. Jafar and W. Khaksar

4 Using RuleML for Representing and Prolog for SimulatingFuzzy Cognitive Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65Athanasios Tsadiras and Nick Bassiliades

5 Fuzzy Web Knowledge Aggregation, Representation, andReasoning for Online Privacy and Reputation Management . . . . . 89Edy Portmann and Witold Pedrycz

6 Decision Making by Rule-Based Fuzzy Cognitive Maps:An Approach to Implement Student-Centered Education. . . . . . . . 107A. Peña-Ayala and J. H. Sossa-Azuela

7 Extended Evolutionary Learning of Fuzzy Cognitive Mapsfor the Prediction of Multivariate Time-Series . . . . . . . . . . . . . . . 121Wojciech Froelich and Elpiniki I. Papageorgiou

8 Synthesis and Analysis of Multi-Step Learning Algorithmsfor Fuzzy Cognitive Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133Alexander Yastrebov and Katarzyna Piotrowska

xi

9 Designing and Training Relational Fuzzy Cognitive Maps . . . . . . 145Grzegorz Słon and Alexander Yastrebov

10 Cooperative Autonomous Agents Based on Dynamical FuzzyCognitive Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Márcio Mendonça, Lúcia Valéria Ramos de Arrudaand Flávio Neves-Jr

11 FCM-GUI: A Graphical User Interface for Big Bang-BigCrunch Learning of FCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177Engin Yesil, Leon Urbas and Anday Demirsoy

12 JFCM : A Java Library for Fuzzy Cognitive Maps . . . . . . . . . . . 199Dimitri De Franciscis

13 Use and Evaluation of FCM as a Tool for Long Term SocioEcological Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221Martin Wildenberg, Michael Bachhofer, Kirsten G. Q. Isakand Flemming Skov

14 Using Fuzzy Grey Cognitive Maps for IndustrialProcesses Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237Jose L. Salmeron and Elpiniki I. Papageorgiou

15 Use and Perspectives of Fuzzy Cognitive Maps in Robotics . . . . . 253Ján Vašcák and Napoleon H. Reyes

16 Fuzzy Cognitive Maps for Structural Damage Detection . . . . . . . 267Ranjan Ganguli

17 Fuzzy Cognitive Strategic Maps . . . . . . . . . . . . . . . . . . . . . . . . . 291M. Glykas

18 The Complex Nature of Migration at a Conceptual Level:An Overlook of the Internal Migration Experience of GebzeThrough Fuzzy Cognitive Mapping Method . . . . . . . . . . . . . . . . 319Tolga Tezcan

19 Understanding Public Participation and Perceptionsof Stakeholders for a Better Management in Danube DeltaBiosphere Reserve (Romania) . . . . . . . . . . . . . . . . . . . . . . . . . . . 355M. N. Vaidianu, M. C. Adamescu, M. Wildenberg and C. Tetelea

xii Contents

20 Employing Fuzzy Cognitive Map for Periodontal DiseaseAssessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375Vijay Kumar Mago, Elpiniki I. Papageorgiou and Anjali Mago

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391

Editor Biography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395

Contents xiii

Contributors

M. C. Adamescu Department of Systems Ecology and Sustainability, Universityof Bucharest, Bucharest, Romania

L. V. R. Arruda Federal University of Technology–Paraná, Av Sete deSetembro, Curitiba, Brazil

M. Bachhofer FCMappers.net, Tokyo, Japan

Nick Bassiliades Department of Informatics, Aristotle University of Thessalo-niki, Thessaloniki, Greece

Dimitri De Franciscis Megadix, Freelance Java/CMS/Database Consultant,Milan, Italy

Anday Demirsoy Control Engineering Department, Faculty of Electrical andElectronics Engineering, Istanbul Technical University, Maslak, Istanbul, Turkey

Wojciech Froelich Institute of Computer Science, University of Silesia,ul.Bedzinska, Sosnowiec, Poland

R. Ganguli Department of Aerospace Engineering, Indian Institute of Science,Bangalore, India

M. Glykas Department of Financial and Management Engineering, University ofthe Aegean, Chios, Greece; Greece Aegean Technopolis, The Technology Park ofthe Aegean Region, Chios, Greece

Steven A. Gray Department of Natural Resources and Environmental Manage-ment, University of Hawaii, Honolulu, HI, USA

Stefan R. J. Gray Coastal and Marine Research Center, University College Cork,Cork, Ireland

K. G. Q. Isak NIRAS, Aarhus, Denmark

N. Ismail Department of Mechanical and Manufacturing, Faculty of Engineer-ing, University Putra Malaysia, Selangor, Malaysia

W. Khaksar Department of Mechanical and Manufacturing, Faculty of Engi-neering, University Putra Malaysia, Selangor, Malaysia

xv

Anjali Mago School of Population and Public Health, University of BritishColumbia, Vancouver, BC, Canada

Vijay Kumar Mago Department of Computer Science, University of Memphis,Memphis, TN, USA

Márcio Mendonça Federal University of Technology–Paraná, Avenue AlbertoCarazzai, Cornélio Procópio, Brazil

Omid Motlagh Department of Robotics and Automation, Faculty of Manufac-turing Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

F. Neves Federal University of Technology–Paraná, Av Sete de Setembro, Cu-ritiba, Brazil

Elpiniki I. Papageorgiou Department of Computer Engineering TE, Techno-logical Educational Institute of Technical University of Central Greece, Lamia,Greece

Witold Pedrycz Department of ECE, University of Alberta, Edmonton, Canada;Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

A. Peña-Ayala WOLNM, ESIME-Z IPN, 31 Julio 1859 Leyes Reforma, Mexico,Iztapalapa, Mexico

Katarzyna Piotrowska Department of Computer Science Applications, KielceUniversity of Technology, Kielce, Poland

Edy Portmann Department of Electrical Engineering and Computer Science,University of California, Berkeley, CA, USA

Napoleon H. Reyes Institute of Information and Mathematical Sciences, MasseyUniversity, Auckland, New Zealand

Jose L. Salmeron Computational Intelligence Lab, University Pablo de Olavide,Seville, Spain

F. Skov Department of Wildlife Ecology and Biodiversity, National Environ-mental Research Institute, Aarhus University, Aarhus, Denmark

Grzegorz Słon Kielce University of Technology, al. Tysiaclecia P. P., Kielce,Poland

J. H. Sossa-Azuela CIC IPN, Mexico, DF, Mexico

S. H. Tang Department of Mechanical and Manufacturing, Faculty of Engi-neering, University Putra Malaysia, Selangor, Malaysia

Tolga Tezcan Scientific and Technological Research Council of Turkey, Kocaeli,Turkey

Athanasios Tsadiras Department of Economics, Aristotle University of Thes-saloniki, Thessaloniki, Greece

xvi Contributors

Leon Urbas Chair of Distributed Control Systems Engineering, Institute ofAutomation, Dresden University of Technology, Dresden, Germany

M. N. Vaidianu CICADIT, University of Bucharest, Regina Elisabeta Blv,Bucharest, Romania

Ján Vašcák Center for Intelligent Technologies, Technical University of Košice,Košice, Slovakia

Martin Wildenberg GLOBAL 2000, Umweltforschungsinstitut, Neustiftgasse,Vienna, Austria

Alexander Yastrebov Department of Computer Science Applications, KielceUniversity of Technology, Kielce, Poland

Engin Yesil Faculty of Electrical and Electronics Engineering, ControlEngi-neering Department, Istanbul Technical University, Maslak, Istanbul, Turkey

E. Zanre Department of Natural Resources and Environmental Management,University of Hawaii, Honolulu, HI, USA

Contributors xvii

Introduction

This book is dedicated to providing readers with deep insights into fundamentals,modeling methodologies, extensions, and learning algorithms for fuzzy cognitivemaps (FCMs), supplemented with codes, software tools, and applications of FCMsin applied sciences and engineering. This will help the academics, new generationresearchers, applied researchers to use the FCM methodology, the severalextensions of FCMs, the FCM learning algorithms, and the new and most availablesoftware tools for modeling, decision making, and support.

The primary goal of this book is to fill the existing gap in the literature andcomprehensively cover the state-of-the-art modeling and learning methods as wellas software tools of FCMs, and provide a set of applications that demonstrate thevarious usages of FCM-based methods and algorithms in the real world.

Description

Fuzzy cognitive maps are fuzzy feedback dynamical systems for modeling causalknowledge. They were introduced by Bart Kosko in 1986 as an extension ofcognitive maps. Cognitive maps are a set of nodes linked by directed and signededges. The nodes represent concepts relevant to a given domain. The causal linksbetween these concepts are represented by the edges which are oriented to showthe direction of the influence and are signed to show a promoting or inhibitoryeffect.

FCM describes a cognitive map model with two significant characteristics. Thefirst one is the type of the causal relationships between concepts which havedifferent intensities represented by fuzzy numbers. A fuzzy number is a quantitywhose value is uncertain, rather than exact. The second one is the systemdynamicity, that is, it evolves with time. It involves feedback, where the effect ofchange in a concept node may affect other concept nodes, which in turn can affectthe node initiating the change. These two characteristics were proposed by Prof.Kosko who is considered the ‘‘father’’ of FCMs.

FCMs have emerged as tools for representing and studying the behavior ofsystems and people. By combining the main aspects of fuzzy logic, neuralnetworks, expert systems, semantic networks, they have gained considerableresearch interest and are widely used to analyze causal complex systems. From anArtificial Intelligence perspective, FCMs are dynamic networks with learning

xix

capabilities, where in more and more data are available to model the problem, thesystem becomes better at adapting itself and reaching a solution. They gainedmomentum due to their dynamic characteristics and learning capabilities. Thesecapabilities make them essential for modeling, analysis, and decision-making tasksas they improve the performance of these tasks. In addition, several FCMextensions have been proposed during the last decade. Each one of them improvesthe conventional FCM in different ways.

During the past decade, FCMs played a vital role in the applications of diversescientific areas, such as social and political sciences, engineering, informationtechnology, robotics, expert systems, medicine, education, prediction, environ-ment, etc. The number of published papers (in the last 10 years) was extremelyhigh and in the last 2 years was exceptionally high showing that there is a stronginterest in FCMs by contemporary researchers. The research in the theory of FCMswas concentrated on providing major improvements and enhancements/extensionsin its theoretical underprinning. Thus, it seems that there is a need for a new bookin the area of FCMs for applied sciences and engineering focusing onfundamentals, extensions, and learning algorithms.

Objective of the Book

This book tries to present emerging trends and advances in FCMs in a concrete andintegrated manner focusing on FCM fundamental methodologies for FCMmodeling, extensions, and learning algorithms for applied sciences and engineer-ing. Also, this book is accompanied with a CD including some main algorithms formodeling and learning algorithms for FCMs, as well as tools and some usefuldemos of developed softwares.

New features of this book:

• Presents systematically and comprehensively the fundamentals of FCM meth-odology, the extensions of FCMs with their theories and learning algorithms,and innovative applications of them as a whole;

• Provides readers with deep insights into dynamical modeling and learningalgorithms, codes, software tools, and applications of FCMs in applied sciencesand engineering;

• Presents different case studies of learning algorithms successfully applied toreal-world problems;

• Provides basic codes and algorithms for FCM-based modeling and learningapproaches, as well as tools and interesting demos.

Target Audience

The audience of this book is both the academic and applied research communitythat has an interest in using FCMs, either as a theoretical framework or as amethodology and tool for applied research, engineering, industrial applications,environmental management, medical decision support, etc. Also, students and newgeneration researchers could be helped and addressed through mathematical andcomputational modeling, as well as learning algorithms for FCMs.

xx Introduction

Book Chapters

This volume is the result of 1 year of effort, where more than 30 chapters wererigorously peer-reviewed by a set of 18 reviewers. (All contributions haveundergone a rigorous review process, involving two independent experts in two tothree rounds of reviews.) After several cycles of chapter submission, revision andtuning based on the Springer international quality principles, 20 works wereapproved, edited as chapters, and organized according to three kinds of topics:Modeling, Learning algorithms and Tools, and Applications. So the first partcorresponds to Modeling and includes Chaps. 1–6 the second part representsLearning Algorithms and Tools and embraces Chaps. 7–13 the third part is relatedto Applications, and contains Chaps. 14–20. A profile/description of the chapters isgiven next:

Chapter 1, written by Elpiniki Papageorgiou and Jose Salmeron, presents thechallenging problem of complex systems modeling, with efficient learning algo-rithms, using methods that utilize existent knowledge and human experience.Special focus is devoted on two issues, methods and learning algorithms for FCMsapplied to modeling and decision-making tasks. A comprehensive survey of thecurrent modeling methodologies and learning algorithms of FCMs is provided.Leading methods and algorithms, concentrated on modeling, are described ana-lytically and analyzed presenting experimental results of a known case studyconcerning process control. The main features of computational methodologies arecompared, highlighting their advantages and limitations, and future researchdirections are outlined.

Chapter 2, written by Steven Gray, E. Zanre and S. R. J. Gray, presents the theo-retical foundations of concept mapping, cognitive mapping, mental models, and thenotion of ‘‘expertise’’ in the elicitation of a subject’s knowledge as they are ofparticular interest on FCM construction and interpretation. It discusses issuesrelated to analyzing FCMs collected from non-traditional experts, which is agrowing area of research that seeks to characterize group knowledge structure toinform community decision-making. To sum up, this chapter addresses how FCMcan be used to understand shared knowledge and what trade-offs should be con-sidered in the selection of FCM data collection techniques.

Chapter 3, written by Omid Motlagh, S. H. Tang, W. Khaksar and N. Ismail,discusses FCM application in relationship modeling context using some agileinference mechanisms. A sigmoid-based activation function for FCM-based rela-tionship model is discussed with application in modeling hexapod locomotion gait.The activation algorithm is then added with a Hebbian weight training technique toenable automatic construction of FCMs. A numerical example case is included toshow the performance of the developed model. The model is examined with per-ceptron learning rule as well. Finally, a real-life example case is tested to evaluatethe final model in terms of FCM relationship modeling.

Introduction xxi

Chapter 4, written by Athanasios Tsadiras and Nick Bassiliades, proposes a newrepresentation scheme of FCMs based on RuleML representation, accompanied bythe implementation of a system that assists decision makers to simulate their owndeveloped Fuzzy Cognitive Maps. The system is designed and implemented inProlog programming language to assist experts to simulate their own FCMs. Thissystem returns results in valid RuleML syntax, making them readily available toother cooperative systems. The design choices of the implemented system arediscussed and the capabilities of the RuleML representation of FCM are presented.The use of the system is exhibited by a number of examples concerning ane-business company.

Chapter 5, written by Edy Portmann and Witold Pedrycz, presents Fuzzy CognitiveMaps as a vehicle for Web knowledge aggregation, representation, and reasoning.The authors introduce a conceptual framework for Web knowledge aggregation,representation, and reasoning, along with a use case, in which the importance ofinvestigative searching for online privacy and reputation is highlighted. Theframework is practicable and robust as solution to seize the presented requirementsof online privacy and reputation management using Fuzzy Cognitive Maps.

In Chap. 6, Alejandro Peña-Ayala and Humberto Sossa apply an extension of thetraditional Fuzzy Cognitive Maps called Rules-based Fuzzy Cognitive Maps(RBFCM). This version depicts the qualitative flavor of the object to be modeledand is grounded on the well-sounded fuzzy logic. A case study in the educationalfield was selected to show the RBFCM usefulness. Their decision-making approachoffers decision-making services to the sequencing module of an intelligent andadaptive web-based educational system (IAWBES). According to the student-centered education paradigm, an IAWBES elicits learners’ traits to adapt lectures toenhance their apprenticeship. This RBFCM-based decision-making approachmodels the teaching scenery, simulates the bias exerted by authored lectures on thestudent’s learning, and picks the lecture option that offers the highest achievement.

In Chap. 7, Wojciech Froelich and Elpiniki Papageorgiou tackle with the issue ofFCM learning using evolutionary algorithms for multivariate time-series predic-tion. Since FCM is a parametric model, it can be trained using historical data.Previous studies have shown that the genetic algorithm can be also used not only foroptimizing the weights of FCM but also for optimization of FCM transformationfunctions. The main idea of this work is to further extend the FCM evolutionarylearning process, giving a special attention on fuzzyfication and transformationfunction optimization, applied in each concept separately, in order to improvethe efficacy of time-series prediction. The proposed extended evolutionaryoptimization process was evaluated in a number of real medical data gathered fromthe Internal Care Unit (ICU). Comparing this approach with other known genet-ic-based learning algorithms, less prediction errors were observed for this dataset.

Chapter 8, written by Alexander Yastrebov and Katarzyna Piotrowska, is devoted tothe analysis of multistep learning algorithms for fuzzy cognitive maps, which are

xxii Introduction

some kind of generalization of known one-step methods of FCM learning. This typeof multi-step learning consists of supervised learning based on gradient method andunsupervised learning type of differential Hebbian learning (DHL) algorithm.These methods were compared with one-step algorithms, from the point of view ofthe influence on the work of the medical prediction system (average percentageprediction error). The analyzed map was initialized and learned based on historicaldata, and then used to predict the clinician’s Parkinson’s disease symptom. Sim-ulation research together with the analysis results was done on prepared softwaretool ISEMK (Intelligent Expert System based on Cognitive Maps).

Chapter 9, written by Grzegorz Słon and Alexander Yastrebov, deals with certainaspects of the design of fuzzy cognitive maps, whose operations are based not on aset of causal rules but on mathematically defined relationships between the modelkey concepts. In such a model, which can be called a relational FCM, the keyconcepts are described by fuzzy numbers, and the relationships between conceptstake the form of specially shaped fuzzy relations. As a result, the operation of themodel is described mathematically by the system of special equations operating onfuzzy numbers and relations. It presents formal and technical difficulties; however,it allows applying certain automation of the process of creating and modifying therelational model of an FCM. It also enables detachment from the rigidly definedlinguistic values in relation to their abstract equivalents, which number can easilybe changed depending on the current needs of the modeling process.

Chapter 10, written by Marcio Mendoza, Lúcia Valéria Ramos de Arruda, andFlávio Neves-Jr, presents an architecture for cooperative autonomous agents basedon dynamic fuzzy cognitive maps (DFCM) that are an evolution of FCMs. Thisarchitecture is used to build an autonomous navigation system for mobile roboticsthat presents learning capacity, online tuning, self-adaptation abilities, andbehaviors management. The navigation system must support the development ofswarm robotics applications. For this, bio-inspired algorithms were used allowingagents to realize tasks. Subsumption architecture was also proposed to manageagent behaviors. Finally, the several DFCMs that correspond to behavioral modulesof an agent were organized in a layered hierarchy of subsumption. The DFCMsplaced in the lower layer present only purely causal relationships and/or fuzzy typerelationships. In the higher layer, the functionalities to adaptation, communicationwith other agents and model evolution are inserted. A multi-agent scheme to shareexperiences among robots was also implemented at the last hierarchy level based onpheromone exchange by ant colony algorithm. The proposed architecture wasvalidated on a simple example of swarm robotics.

The need of developing novel approaches for an automated generation of FCMsusing historical data is the focus of Chap. 11. Engin Yesil, Leon Urbas and AndayDemirsoy, present a software development focusing on a learning method forFCMs. A new optimization algorithm, which is called Big Bang-Big Crunch (BB-BC), is proposed for an automated generation of FCMs from data. Moreover, a

Introduction xxiii

graphical user interface (GUI) is designed and an FCM-GUI is developed usingMatlab. In this study, two real-world examples; namely a process control systemand a synthetic model generated by the proposed FCM-GUI are used to emphasizethe effectiveness and usefulness of the proposed methodology. The results of thestudied examples show the efficiency of the developed FCM-GUI for design,simulation, and learning of FCMs.

Chapter 12 presents Java Fuzzy Cognitive Maps (JFCM) developed by Dimitri DeFranciscis. JFCM is an open source library that implements fuzzy cognitive mapsusing the JavaTM programming language. Dimitri De Franciscis introduces thelibrary and its main features, along with many code examples and experiments thatshow how to effectively use it in projects. The proposed library is a simple,standalone library written in Java, so it can run on many operating systems, has veryfew dependencies on other libraries, and is released under LGPL license, whichpermits inclusion in commercial projects. Due to these advantages, the library canbe used to build a wide range of cognitive maps.

In Chap. 13, Martin Wildenberg, Michael Bachhofer, Kirsten G. Q. Isak, andFlemming Skov, apply Fuzzy Cognitive Mapping as a tool to support conservationmanagement. As part of ALTER-Net, FCM was applied to five cases and subse-quently was evaluated by means of a SWOT framework. This approach examinesthe strengths and weaknesses of, and the opportunities and threats to FCM whenapplied in conservation management which is dealing with landscapes as socio-ecological systems. Moreover, the FCMapper (see www.fcmappers.net) softwarefor FCM modeling and analysis, is presented and used. This software is freelyavailable, based on excel and allows to calculate the basic FCM indices, conductdynamical analysis, and visualize the fuzzy cognitive maps.

Chapter 14, written by Jose Salmeron and Elpiniki Papageorgiou, applies FuzzyGrey Cognitive Maps (FGCM) for process problems in industry. FGCM, as anextension of Fuzzy Cognitive Maps, mixing fuzzy logic, grey systems theory, andcognitive map theories, is capable of dealing with uncertain problems and humanreasoning as well. It is an innovative approach for modeling complex systems andthus it is used to elaborate on industrial process control. The developed FGCMmodel is composed of grey nodes that represent the main factors involving thecontrol problem linked by directed grey edges that show the cause-effect rela-tionships between them. This FGCM model is improved using the NHL learningalgorithm. Some experiments are conducted showing the effectiveness, validity,and especially the advantageous behavior of the proposed grey-based methodologyof constructing and learning FCMs.

The use and perspectives of Fuzzy Cognitive Maps in Robotics is the focus ofChap. 15. This chapter, written by Jan Vascak and Napoleon H. Reyes, deals withspecification of needs for a robot control system and divides defined tasks into threebasic decision levels dependent on their specification of use as well as applied

xxiv Introduction

means. Concretely, examples of several FCMs applications from the low andmiddle decision levels are described, mainly in the area of navigation, movementstabilization, action selection, and path cost evaluation. FCMs are applied inrobotics to offer an overview of potential possibilities for this perspective means ofartificial intelligence. FCMs can find use not only on higher decision levels, whichrequire a certain measure of intelligence, but also on lower levels of control, whereconventional PID controllers are used very often. Actually, a rough survey of theuse of FCMs in robotics is given.

In Chap. 16, Ranjan Ganguli applies Fuzzy Cognitive Maps for structural damagedetection. Structures such as bridges, buildings, nuclear power plants, aircraft,helicopters, turbines, vehicles etc., constitute a key component of modern engi-neering and economic infrastructure. However, such structures are susceptible todamage due to the environment and harsh operating conditions. Damage in struc-tures can lead to degradation in performance and potential catastrophic failure.Therefore, there is a need on algorithms for accurate structural damage detection.This chapter applies FCMs with the efficient nonlinear Hebbian learning algorithmfor detecting structural damage in a cantilever beam from measured natural fre-quencies. Numerical simulations from a finite element model are used to create afuzzy rule base which is used to design the FCM. Hebbian learning allows the FCMto improve itself and the numerical results show that the FCM with Hebbianlearning is a robust tool for damage detection with noisy data.

In Chap. 17, Michael Glykas focus on the presentation and use of fuzzy strategicmaps. He presents the theoretical framework of FCM and its associated modelingand simulation tool to Strategy Maps (SMs). Strategy maps represent visuallyrelationships among the key components of an organization’s strategy. They arepowerful tools which show how value is created through cause and effect rela-tionships. Some main limitations of the scenario based SMs are the inheritedinability to change scenarios dynamically as well as the missing element of time.FCMs are presented as an alternative to overcome these shortfalls with the intro-duction of fuzziness in their weights and the robust calculation mechanism. AnFCM tool is presented that allows simulation of SMs as well as interconnection ofnodes (performance measures) in different SMs which enables the creation of SMhierarchies. An augmented FCM calculation mechanism that allows this type ofinterlinking is also presented. The resulting methodology and tool are applied totwo Banks and the results of these case studies are presented.

In Chap. 18, Tolga Teczan handles the issue of migration at a conceptual level.Migration is generally considered as a demographic event and many studies havetried to investigate how social dynamics and identities play a role in the migrationphenomenon in urban areas. However, none of them have analyzed this through amodel that allows presenting a perception towards migrants and the phenomenon ofmigration from the point of view of social groups at a conceptual and relational

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level. In this study, FCM approach is presented in order to understand historicalexperiences of migration that have reflections on daily practices and identify thecausal characteristics of the migration issue. Through this proposal, it is possible toobserve how social inequalities caused by and/or leading to migration becomevisible and more comprehensive from the perspective of different social categoriesof migrants.

In Chap. 19, Maria-Natas�a Vaidianu, Mihai Cristian Adamescu, Martin Windelbergand Cristian Tetelea, examine the perceptions of local stakeholders in SfantuGheorghe, Danube Delta Biosphere Reserve (DDBR), Romania, with the aim ofdeveloping key concepts that will be used in future information and communicationstrategies regarding economic characteristics, sustainable development and biodi-versity conservation in the area. FCM approach was applied to help people gainknowledge, values, and the awareness they need to manage efficiently environ-mental resources and to assume responsibility for maintaining environmentalquality. For this, 30 cognitive maps were developed together with stakeholders.Analysis reveals that DDBR Administration, county authorities, and local author-ities are substantially worried about the pollution and overfishing, while other socialgroups care more about touristic activities, accessibility degree, health system, orfinancial resources.

In Chap. 20, Vijay Mago, Elpiniki Papageorgiou and Anjali Mago, elaborate on theuse of FCMs as a decision support mechanism for periodontal disease assessment,and with the development of a software tool for supporting dentists in makingdecisions. An FCM approach accompanied with an easy to use software system toassess the severity level of periodontal disease in dental patients was proposed. Theorigin of this work is based on the application of FCM methodology to assessuncertainty inherent in the medical domain thus deciding the severity of periodontaldisease and the cause of the disease. Dentist usually relies on his knowledge,expertise, and experiences to design the treatment(s). Therefore, it is found thatthere is a variation among treatments administered by different dentists. The causalrelationships between different sign-symptoms have been defined using easilyunderstandable linguistic terms following the construction process of FCM andthen converted to numeric values using Mamdani inference method. Also, a soft-ware tool with an easy to use Graphical User Interface (GUI) for dentists wasdeveloped. The tool can also benefit the non-specialized dentists in decision makingin their daily practice.

This volume covers an important range of FCM modeling methods, extensions,learning algorithms, and software tools in its 20 chapters. Modeling advancesinclude new relationship modeling laws, new designing methods using non-traditional experts and web knowledge, new causal learning laws and the use ofdynamic features such as cooperative autonomous agents for concepts, andweighted edges to establish dynamic fuzzy cognitive maps. Learning advances

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include new and extended evolutionary learning approaches, multi-step learningalgorithms for prediction and decision making, multi-agents for navigation tasks.The FCMs with their advanced methodologies found applications on engineering,control, management, environmental modeling, decision making, medical decisionsupport, among many others.

Also, a CDROM is provided with this volume, which contains 14 resources fromthe respective chapters, devoted to FCM-based modeling and learning algorithms(six codes), software tools that allow the design, simulation, learning, and resultreporting of FCM (six software packages), as well as some interesting demos.

Thanks

I would like to sincerely thank from the bottom of my heart, the ‘‘Inspirer’’ of FuzzyCognitive Map, Prof. Bart Kosko, who accepted my invitation to contribute to theforeword of this book, providing a new insight and vision to the field. It is a deephonor for me and for all the contributors to this book.

Moreover, I wish to express my gratitude to all the contributors of this book forpresenting their research in an easily accessible manner, and for putting such dis-cussion into a historical context. Without them, this work would not be accom-plished.

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