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Links that will help you getting into FCM and others giving you new ideas for what is possible with this method.

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1   Link   Group Decision Support Using Fuzzy Cognitive Maps for Causal Reasoning
Tags: FCM, group decision support, causal influence

Cognitive maps have been used for analysing and aiding decision-making by investigating causal links among relevant domain concepts. A fuzzy cognitive map (FCM) is an extension of a cognitive map with the additional capability of representing feedback through weighted causal links. FCMs can be used as tools for both static as well as dynamic analysis of scenarios evolving with time. An FCM represents an expert's domain knowledge in a form that lends itself to relatively easy integration into a collective knowledge base for a group involved in a decision process. The resulting group FCM has the potential to serve as a useful tool in a group decision support environment. An appropriate methodology for the development and analysis of group FCMs is required. A framework for such a methodology consisting of the development and application phases is presented.
2   Link   Application of fuzzy sets and cognitive maps to incorporate social science scenarios in integrated assessment modelsA case study of urbanization in Ujung Pandang, Indonesia
Tags: Integrated Assessment, decision–support systems, fuzzy set theory, fuzzy cognitive maps, scenario analysis

Authors: Jean-Luc de Kok, Milan Titus and Herman G. Wind
Journal: Integrated Assessment (2004)
Decision–support systems in the field of integrated water management could benefit considerably from social science knowledge, as many environmental changes are human-induced. Unfortunately the adequate incorporation of qualitative social science concepts in a quantitative modeling framework is not straightforward. The applicability of fuzzy set theory and fuzzy cognitive maps for the integration of qualitative scenarios in a decision–support system was examined for the urbanization of the coastal city of Ujung Pandang, Indonesia. The results indicate that both techniques are useful tools for the design of integrated models based on a combination of concepts from the natural and social sciences.
3   Link   Application of fuzzy cognitive maps to factors affecting slurry rheology
Tags: FCM, rheology, viscosity

Authors: G. A. Banini and R. A. Bearman
Journal: International Journal of Mineral Processing (1998)

We propose fuzzy cognitive maps, a branch of fuzzy logic, to study interaction of factors affecting processes and details of the approach are discussed. Application of the technique to discriminate between factors affecting slurry rheology is demonstrated. It has been shown that hydrodynamic interaction, effective particle concentration, shape and size, temperature and shear rate have a significant influence on the slurry viscosity. The complex interaction of the various factors delineated by previous workers is also presented.
4   Link   Using fuzzy cognitive maps as a system model for failure modes and effects analysis

Authors: C. Enrique Peláez and John B. Bowles
Journal: Intelligent system (1996)

This paper explores the application of Fuzzy Cognitive Maps (FCM) to Failure Modes and Effects Analysis (FMEA). FMEAs are used in reliability and safety evaluations of complex systems to determine the effects of component failures on the system operation. FCMs use a digraph to show cause and effect relationships between concepts; thus, they can represent the causal relationships needed for the FMEA and provide a new strategy for predicting failure effects in a complex system.
5   Link   Application of fuzzy cognitive maps for cotton yield management in precision farming
Tags: Fuzzy cognitive maps; Modeling; Expert knowledge; Learning algorithm; Unsupervised learning; Decision making; Cotton; Yield; Soil

Authors: Elpiniki I. Papageorgiou, Athanasios Markinos, and Theofanis Gemptosb
Journal: Expert Systems with Applications (2009)

Abstract: The management of cotton yield behavior in agricultural areas is a very important task because it influences and specifies the cotton yield production. An efficient knowledge-based approach utilizing the method of fuzzy cognitive maps (FCMs) for characterizing cotton yield behavior is presented in this research work. FCM is a modelling approach based on exploiting knowledge and experience. The novelty of the method is based on the use of the soft computing method of fuzzy cognitive maps to handle experts’ knowledge and on the unsupervised learning algorithm for FCMs to assess measurement data and update initial knowledge.

The advent of precision farming generates data which, because of their type and complexity, are not efficiently analyzed by traditional methods. The FCM technique has been proved from the literature efficient and flexible to handle experts’ knowledge and through the appropriate learning algorithms can update the initial knowledge. The FCM model developed consists of nodes linked by directed edges, where the nodes represent the main factors in cotton crop production such as texture, organic matter, pH, K, P, Mg, N, Ca, Na and cotton yield, and the directed edges show the cause–effect (weighted) relationships between the soil properties and cotton field.

The proposed method was evaluated for 360 cases measured for three subsequent years (2001, 2003 and 2006) in a 5 ha experimental cotton yield. The proposed FCM model enhanced by the unsupervised nonlinear Hebbian learning algorithm, was achieved a success of 75.55%, 68.86% and 71.32%, respectively for the years referred, in estimating/predicting the yield between two possible categories (“low” and “high”). The main advantage of this approach is the sufficient interpretability and transparency of the proposed FCM model, which make it a convenient consulting tool in describing cotton yield behavior.
6   Link   Brain tumor characterization using the soft computing technique of fuzzy cognitive maps
Tags: Soft computing; Computational intelligence; Fuzzy logic; Fuzzy cognitive maps; Classification; Brain tumors

Authors: E.I. Papageorgioua, P.P. Spyridonosc, D. Th. Glotsosc, C.D. Styliosb, , P. Ravazoulad, G.N. Nikiforidisc and P.P. Groumposa
Journal: Applied Soft Computing (2008)

The characterization and accurate determination of brain tumor grade is very important because it influences and specifies patient's treatment planning and eventually his life. A new method for characterizing brain tumors is presented in this research work, which models the human thinking approach and the classification results are compared with other computational intelligent techniques proving the efficiency of the proposed methodology. The novelty of the method is based on the use of the soft computing method of fuzzy cognitive maps (FCMs) to represent and model experts’ knowledge (experience, expertise, heuristic). The FCM grading model classification ability was enhanced introducing a computational intelligent training technique, the Activation Hebbian Algorithm. The proposed method was validated for clinical material, comprising of 100 cases. FCM grading model achieved a diagnostic output of accuracy of 90.26% (37/41) and 93.22% (55/59) for brain tumors of low-grade and high-grade, respectively. The results of the proposed grading model present reasonably high accuracy, and are comparable with existing algorithms, such as decision trees and fuzzy decision trees which were tested at the same type of initial data. The main advantage of the proposed FCM grading model is the sufficient interpretability and transparency in decision process, which make it a convenient consulting tool in characterizing tumor aggressiveness for every day clinical practice.
7   Link   Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links

Authors: Elpiniki I. Papageorgiou, Chrysostomos Styliosb and Peter P. Groumpos

Journal: International Journal of Human-Computer Studies (2006)

Fuzzy Cognitive Maps (FCMs) constitute an attractive knowledge-based methodology, combining the robust properties of fuzzy logic and neural networks. FCMs represent causal knowledge as a signed directed graph with feedback and provide an intuitive framework which incorporates the experts’ knowledge. FCMs handle available information and knowledge from an abstract point of view. They develop behavioural model of the system exploiting the experience and knowledge of experts. The construction of FCMs is based mainly on experts who determine the structure of FCM, i.e. concepts and weighted interconnections among concepts. But this methodology may not be a sufficient model of the system because the human factor is not always reliable. Thus the FCM model of the system may requires restructuring which is achieved through adjustment the weights of FCM interconnections using specific learning algorithms for FCMs. In this article, two unsupervised learning algorithms are presented and compared for training FCMs; how they define, select or fine-tuning weights of the causal interconnections among concepts. The implementation and results of these unsupervised learning techniques for an industrial process control problem are discussed. The simulations results of training the process system verify the effectiveness, validity and advantageous characteristics of those learning techniques for FCMs.
8   Link   Linking stakeholders and modellers in scenario studies: The use of Fuzzy Cognitive Maps as a communication and learning tool
Tags: integrative scenario studies, participatory scenario development, semi-quantitative, FCM

Authors: Mathijs van Vliet Kasper Koka and Tom Veldkampa
Journal: Futures (2009)

Within large integrative scenario studies, it is often problematic to fully link narrative storylines and quantitative models. This paper demonstrates the potential use of a highly participatory scenario development framework that involves a mix of qualitative, semi-quantitative and quantitative methods. The assumption is that the use of semi-quantitative methods will structure the participatory output, which provides a solid base for quantification. It should further facilitate the communication between stakeholders and modellers. Fuzzy Cognitive Maps is the main semi-quantitative method and has a central place in the proposed framework. The paper provides a detailed description of its implementation in participatory workshops, also because of a lack of documented testing of its implementation. We tested Fuzzy Cognitive Maps as part of the framework in two training sessions; both gave encouraging results. Results show that the tool provides a structured, semi-quantitative understanding of the system perceptions of a group of participants. Participants perceived the method as easy to understand and easy to use in a short period of time. This supports the hypothesis that Fuzzy Cognitive Maps can be used as part of a scenario development framework and that the new framework can help to bridge the gap between storylines and models.
9   Link   Modelling IT projects success with Fuzzy Cognitive Maps
Tags: Fuzzy Cognitive Maps; Critical Success Factors; IT projects; Mobile Payment System; Telecommunications

Authors: Luis Rodriguez-Repisoa, Rossitza Setchia, and Jose L. Salmeronb
Journal: Expert Systems with Applications (2007)

IT projects have certain features that make them different from other engineering projects. These include increased complexity and higher chances of project failure.

To increase the chances of an IT project to be perceived as successful by all the parties involved in the project from its conception, development and implementation, it is necessary to identify at the outset of the project what the important factors influencing that success are.

Current methodologies and tools used for identifying, classifying and evaluating the indicators of success in IT projects have several limitations that can be overcome by employing the new methodology presented in this paper. This methodology is based on using Fuzzy Cognitive Maps (FCMs) for mapping success, modelling Critical Success Factors (CSFs) perceptions and the relations between them. This is an area where FCM has never been applied before. The applicability of the FCM methodology is demonstrated through a case study based on a new project idea, the Mobile Payment System (MPS) project, related to the fast evolving world of mobile telecommunications.
10   Link   Fuzzy cognitive maps
Tags: FCM, causal reasoning, soft knowledge

Author: Bart Kosko
Journal: International Journal of Man-Machine Studies (1986)

- this is the first article published on FCM -

Fuzzy cognitive maps (FCMs) are fuzzy-graph structures for representing causal reasoning. Their fuzziness allows hazy degrees of causality between hazy causal objects (concepts). Their graph structure allows systematic causal propagation, in particular forward and backward chaining, and it allows knowledge bases to be grown by connecting different FCMs. FCMs are especially applicable to soft knowledge domains and several example FCMs are given. Causality is represented as a fuzzy relation on causal concepts. A fuzzy causal algebra for governing causal propagation on FCMs is developed. FCM matrix representation and matrix operations are presented in the Appendix.
A searchable vesion could be found here-> http://www.sciencedirect.com/science/article/pii/S0020737386800402
11   Link   Augmented fuzzy cognitive maps for modelling LMS critical success factors
Tags: Augmented Fuzzy Cognitive Map, FCM, Critical Success Factors, decision making, learning

Authors: Jose L. Salmeron
Journal: Knowledge-Based Systems (2009)

This paper proposes to build an Augmented Fuzzy Cognitive Map-based for modelling Critical Success Factors in Learning Management Systems. The study of Critical Success Factors helps decision makers to extract from the multidimensional learning process the core activities that are essential for success. Using Fuzzy Cognitive Maps for modelling Critical Success Factors provides major assistance to the e-learning community, by permitting prediction comparisons to be made between numerous tools measured by multiple factors and its relations.
12   Link   Mapping knowledge management and organizational learning in support of organizational memory
Tags: Knowledge management; Organizational learning; Fuzzy cognitive mapping; FCM; Case study

Authors: Zahir Irania, Amir M. Sharifb and Peter E.D. Loveb
Journal: International Journal of Production Economics (2009)

The normative literature within the field of knowledge management has concentrated on techniques and methodologies for allowing knowledge to be codified and made available to individuals and groups within organizations. The literature on organizational learning, however, has tended to focus on aspects of knowledge that are pertinent at the macro-organizational level (i.e. the overall business). The authors attempt in this paper to address a relative void in the literature, aiming to demonstrate the inter-locking factors within an enterprise information system that relate knowledge management and organizational learning, via a model that highlights key factors within such an inter-relationship. This is achieved by extrapolating data from a manufacturing organization using a case study, with these data then modeled using a cognitive mapping technique (fuzzy cognitive mapping, FCM). The empirical enquiry explores an interpretivist view of knowledge, within an information systems evaluation (ISE) process, through the associated classification of structural, interpretive and evaluative knowledge. This is achieved by visualizing inter-relationships within the ISE decision-making approach in the case organization. A number of decision paths within the cognitive map are then identified such that a greater understanding of ISE can be sought. The authors therefore present a model that defines a relationship between knowledge management (KM) and organizational learning (OL), and highlights factors that can lead a firm to develop itself towards a learning organization.
13   Link   Learning Fuzzy Cognitive Maps from the Web for the Stock Market Decision Support System

Authors: Wojciech Froelichand Alicja Wakulicz-Deja
Book: Advances in Intelligent Web Mastering (2007)

In this paper we would like to propose a new hybrid scheme for learning approximate concepts and causal relations among them using information available from the Web. For this purpose we are applying fuzzy cognitive maps (FCMs) as a knowledge representation method and an analytical tool. Fuzzy cognitive maps are a decision-support tool, analytical technique, and a qualitative knowledge representation method with large potential for real world applications. FCMs are able to express the behavior of a system through the description of cause and effect relationships among concepts. FCMs can be represented as directed graphs consisting of concepts (nodes) and cause and effect relationships (branches) among them. The concepts represent states that are observable within the domain. The directions of branches indicate the causal dependency between source and target concepts. In spite of a quite simple construction and relatively easy interpretation, which can play a key role while constructing decision support systems, its expected that FCMs can express complex behaviors of dynamic systems. The basic formalism of FCMs is presented in section 2. Obviously, there are also drawbacks of FCMs, that have been mentioned, e.g., in [4]. Also, it can be mentioned that, among the many extensions to FCMs, there is still lack of common formalism, which causes some difficulties when comparing one with another.
14   Link   Fuzzy cognitive map architectures for medical decision support systems
Tags: Medical decision support systems; Fuzzy cognitive maps

Authors: Chrysostomos D. Styliosa, Voula C. Georgopoulosb, Georgia A. Malandrakic and Spyridoula Chouliarad

Journal: Applied Soft Computing

Medical decision support systems can provide assistance in crucial clinical judgments, particularly for inexperienced medical professionals. Fuzzy cognitive maps (FCMs) is a soft computing technique for modeling complex systems, which follows an approach similar to human reasoning and the human decision-making process. FCMs can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system. Medical decision systems are complex systems that can be decomposed to non-related and related subsystems and elements, where many factors have to be taken into consideration that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall clinical decision with a different degree. Thus, FCMs are suitable for medical decision support systems and appropriate FCM architectures are proposed and developed as well as the corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.
15   Link   A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps
Tags: Fuzzy Cognitive Maps; Learning algorithms; Nonlinear Hebbian rule; Evolutionary computation; Differential evolution algorithms; Evolutionary training

Author: Elpiniki I. Papageorgiou, and Peter P. Groumpos

Journal: Applied Soft Computing

A novel hybrid method based on evolutionary computation techniques is presented in this paper for training Fuzzy Cognitive Maps. Fuzzy Cognitive Maps is a soft computing technique for modeling complex systems, which combines the synergistic theories of neural networks and fuzzy logic. The methodology of developing Fuzzy Cognitive Maps relies on human expert experience and knowledge, but still exhibits weaknesses in utilization of learning methods and algorithmic background. For this purpose, we investigate a coupling of differential evolution algorithm and unsupervised Hebbian learning algorithm, using both the global search capabilities of Evolutionary strategies and the effectiveness of the nonlinear Hebbian learning rule. The use of differential evolution algorithm is related to the concept of evolution of a number of individuals from generation to generation and that of nonlinear Hebbian rule to the concept of adaptation to the environment by learning. The hybrid algorithm is introduced, presented and applied successfully in real-world problems, from chemical industry and medicine. Experimental results suggest that the hybrid strategy is capable to train FCM effectively leading the system to desired states and determining an appropriate weight matrix for each specific problem.
16   Link   Soft computing in medicine
Tags: Soft computing; Fuzzy–neural systems; Fuzzy–genetic algorithms; Neural–genetic algorithms; Medicine

Author: Ahmet Yardimci

Journal: Applied Soft Computing

Soft computing (SC) is not a new term; we have gotten used to reading and hearing about it daily. Nowadays, the term is used often in computer science and information technology. It is possible to define SC in different ways. Nonetheless, SC is a consortium of methodologies which works synergistically and provides, in one form or another, flexible information processing capability for handling real life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to achieve tractability, robustness and low-cost solutions. SC includes fuzzy logic (FL), neural networks (NNs), and genetic algorithm (GA) methodologies. SC combines these methodologies as FL and NN (FL–NN), NN and GA (NN–GA) and FL and GA (FL–GA). Recent years have witnessed the phenomenal growth of bio-informatics and medical informatics by using computational techniques for interpretation and analysis of biological and medical data. Among the large number of computational techniques used, SC, which incorporates neural networks, evolutionary computation, and fuzzy systems, provides unmatched utility because of its demonstrated strength in handling imprecise information and providing novel solutions to hard problems.

The aim of this paper is to introduce briefly the various SC methodologies and to present various applications in medicine between the years 2000 and 2008. The scope is to demonstrate the possibilities of applying SC to medicine-related problems. The recent published knowledge about use of SC in medicine is researched in MEDLINE. This study detects which methodology or methodologies of SC are used frequently together to solve the special problems of medicine. According to MEDLINE database searches, the rates of preference of SC methodologies in medicine were found as 68% of FL–NN, 27% of NN–GA and 5% of FL–GA. So far, FL–NN methodology was significantly used in medicine. The rates of using FL–NN in clinical science, diagnostic science and basic science were found as %83, %71 and %48, respectively. On the other hand NN–GA and FL–GA methodologies were mostly preferred by basic science of medicine.

Another message emerging from this survey is that the number of papers which used NN–GA methodology has continuously risen until today. Also search results put the case clearly that FL–GA methodology has not applied well enough to medicine yet. Undeniable interest in studying SC methodologies in genetics, physiology, radiology, cardiology, and neurology disciplines proves that studying SC is very fruitful in these disciplines and it is expected that future researches in medicine will use SC more than it is used today to solve more complex problems.
17   Link   A new method of soft computing to estimate the economic contribution rate of education in China
Tags: Soft computing; Fuzzy neural networks; The economic contribution rate of education, China

Author: Haixiang Guo, Fengqin Diaoa, Kejun Zhua, Jinling Lia and Yanmin Xinga

Journal: Applied Soft Computing

Economic contribution rate of education (ECE) is the key factor of education economics. In this paper, a soft computing method of economic contribution rate of education is proposed. The method is composed of four steps. The first step does fuzzy soft-clustering to object system based on levels of science and technology and obtains the optimal number of clusters, which determines the number of fuzzy rules. The second step constructs the fuzzy neural networks FNN1 from human capital to economic growth and obtains economic contribution rate of human capital ?k. The third step constructs the fuzzy neural networks FNN2 from education to human capital and obtains human capital contribution rate of education ??k. The fourth step calculates the economic contribution rate of education ECEk=?k×??k. At last, this algorithm is applied to obtain the economic contribution rate of education in China.

18   Link   Using Qualitative Evidence to Enhance an Agent-Based Modelling System for Studying Land Use Change
Authors: J. Gary Polhill, Lee-Ann Sutherland and Nicholas M. Gotts

Agent-Based Modelling, Land Use/Cover Change, Qualitative Research, Interdisciplinary Research

This paper describes and evaluates a process of using qualitative field research data to extend the pre-existing FEARLUS agent-based modelling system through enriching its ontological capabilities, but without a deep level of involvement of the stakeholders in designing the model itself. Use of qualitative research in agent-based models typically involves protracted and expensive interaction with stakeholders; consequently gathering the valuable insights that qualitative methods could provide is not always feasible. At the same time, many researchers advocate building completely new models for each scenario to be studied, violating one of the supposed advantages of the object-oriented programming languages in which many such systems are built: that of code reuse. The process described here uses coded interviews to identify themes suggesting changes to an existing model, the assumptions behind which are then checked with respondents. We find this increases the confidence with which the extended model can be applied to the case study, with a relatively small commitment required on the part of respondents.

19   Link   special issue in Ecology & Society: Mental Models in Human Environment interactions: theory, policy implications, and methodological explorations
A collection of 6 articles with an overview and some examples for the use of mental models in social ecological research.


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