Fuzzy Cognitive Maps Methodology Research Articles

Correlates of spatial structure variability in Bushehr port-city: A comprehensive analysis using fuzzy cognitive mapping methodology

It is critical for urban and regional planners to examine spatial relationships and interactions between a port and its surrounding urban areas within a region’s spatial structure. This paper seeks to develop a targeted framework of causal relationships influencing the spatial structure changes in the Bushehr port-city. Hence, the study utilizes Fuzzy Cognitive Maps (FCMs), a computational technique adept at analyzing complex decision-making processes. FCMs are employed to identify concepts that act as drivers or barriers in the spatial structure changes of Bushehr port-city, thereby elucidating the causal relationships within this context. Additionally, the study evaluates these concepts’ relative significance and interrelationships. Data was collected through interviews with ten experts from diverse backgrounds, including specialists, academics, policymakers, and urban managers. The insights from these experts were analyzed using FCMapper and Pajek software to construct a collective FCM, which depicts the influential and affected concepts within the system. The resulting collective FCM consists of 16 concepts, representing the varied perspectives and expertise of the participants. Among these, the concepts of management and planning reform, economic growth of the city-port, and port development emerged as the three most central concepts. Moreover, the effects of all influential concepts on the spatial structure change in Bushehr port-city were evaluated through simulations conducted across four different scenarios. The analysis demonstrated that the system experiences the most significant impact under the fourth scenario, where the most substantial changes are observed in commercial and industrial growth and the planning of port-city separation policies.

Examining the Relationship between Risks Associated with Public-Private Partnership Projects in Emerging Markets Using Fuzzy Cognitive Mapping Methodology

Objective: The adoption of Public-Private Partnerships (PPPs) has sparked a surge in research interest in this area in recent years. This study aims to identify and prioritize the risks associated with PPP projects. Methodology: A questionnaire was designed to assess the significance of each of the 35 research variables (risks) derived from the literature in explaining the risks associated with PPP projects. The respondents included experts in risk associated with PPP-based projects in emerging markets, specializing in policy making, production, sales, and export. These experts were considered knowledgeable due to their practical experience in management, expert, academic, and research activities related to risks and emerging projects and markets. Using purposive sampling and following a snowball method, semi-structured in-depth interviews were conducted with 19 experts until theoretical saturation was achieved. Findings: The results were analyzed using the methodology of fuzzy cognitive maps, and the relationships between the research factors were examined. Conclusion: The analysis revealed that financial constraints risk, demand risk, and government support risk are of high importance in the context of PPP projects.

A fuzzy cognitive map approach to understand agricultural system and food prices in Türkiye: Policy recommendations for national food security

AbstractOnce one of the few self‐sufficient food countries in the world, Turkey has become dependent on imports to feed its population. Food prices have climbed to among the highest in the world, severely threatening the food security of the country. Most researchers generally attributed the high prices to the increased input costs of agriculture. Although the role of input prices cannot be denied, this paper focuses on a neglected problem that can account for food price inflation: the attitudes and behaviours of farming communities towards agriculture. Through fuzzy cognitive map methodology, known to be very effective in understanding complex networks of problems, we identify and map the relationships among the factors affecting the agriculture system, develop interview and literature‐driven scenarios, and test these scenarios to demonstrate their role in explaining the relationship between attitudes and behaviours of farming communities and food prices in Turkey. Our findings provide recommendations to policymakers.

A Review of Fuzzy Cognitive Maps Extensions and Learning

Fuzzy Cognitive Maps (FCM) is a soft computing technique whose vertices and edges are fuzzy values with an inference mechanism for solving modelling problems; it has been used in modelling complex systems like industrial and process control. The concept was first introduced in 1986, with an initial learning algorithm in 1996; several works have been published on FCM methodology, learnings and applications. Fuzzy cognitive maps continue to evolve both in theory, learning algorithms and application. Many theories like intuitionistic theory, hesitancy theory, grey system theory, wavelet theory, etc., are integrated with the conventional FCM. These extensions have improved Fuzzy cognitive Maps to handle problems of uncertainty, incomplete information, hesitancy, dynamic systems and probabilistic fuzzy events. They also strengthen fuzzy cognitive Maps’ modelling power for application in almost any domain. However, the compilation of the development in methodology and adaptation of FCM are either old or omitted some of the recent advances or focused on specific applications of FCM in some areas. This paper reports extension, learning and applications of FCM from the initial conventional FCM to recent extensions and some of the important features of those extensions and learning.

Bioeconomy Sustainability Index: A Fuzzy Cognitive Mapping Approach

Abstract There is no one unique parameter with value which can represent bioeconomy sustainability and development. However, many different parameters can be used to describe the economic, social, environmental technological aspects of bioeconomy. The paper present combined approach with using multi criteria decision analysis (MCDA) and fuzzy cognitive map (FCM) analysis methods for bioeconomy sustainability index. Study describe methodology of fuzzy cognitive map developing, including expert involving, obtained data from survey combination to bioeconomy indicators and categories. The weight of selected FCM components were normalized using MCDA method. The critical points of the applied method and opportunities for improvement have been identified. The role of respondents in the results is also considered.

An integrated Fuzzy DEMATEL and Fuzzy Cognitive Maps approach for the assessing of the Industry 4.0 Model

Today, organizations are experiencing problems due to advancements in the environment, community, economy, and technology. In the future, physical and virtual systems, including the whole chain from product design to the distribution to customers and feedback will be required to overcome problems for organizations. Thus, companies need to comprehend and apply Industry 4.0. The most frequently encountered situation in the literature is the absence of research based on the practical experience of Industry 4.0 processes. In this study, the analysis of 9 concepts (Strategy, Leadership, Customers, Products, Operations, Culture, People, Governance, and Technology) is performed by utilizing the fuzzy DEMATEL and fuzzy AHP methods. As a result of both methods, Technology, Operations and Strategy concepts are obtained as the three most important concepts in the same rank in both methods. There is no significant change in the rank of the other concepts. Although close rankings are obtained from both methods, considering the interaction between the concepts and avoid shortcomings of consensus in the method, the ranking obtained by the fuzzy DEMATEL method is preferred to use as input in Fuzzy Cognitive Maps (FCMs). Then a relation map is formed based on their interaction and three alternative scenarios are created for FCMs from defuzzified total relation matrix data. As a result of scenarios, it is more useful for organizations to apply the Industry 4.0 processes to transfer their resources to their activities concerning the Strategies, Technology, and Operations concepts. The aim is to predict the maturity levels of Industry 4.0 of organizations for the future by employing the FCMs methodology. Thanks to the introduced model based on the integration of fuzzy DEMATEL and FCMs, an insight is provided into which concepts should be prioritized about Industry 4.0.

Design and development a model to present practices for implementation cloud manufacturing system

The purpose of this paper is to identify and classify the main factors implementing the cloud manufacturing systems in the internet service providers company by fuzzy cognitive map methodology. Through expert opinions, 20 main factors were identified and classified based on the importance and then a fuzzy cognitive map approach was applied for obtaining the relationship between the factors, and all of the impact factors are outputs of expert opinion. The outcomes of the study highlighted that three factors, including customer scoring, R&D, and method development were the most important factors impressing the implementation cloud manufacturing systems. Present practices for implementation cloud manufacturing system is and the relationship between the main factors also impressing cloud manufacturing system by employing the fuzzy cognitive map approach in the Iranian internet service provider company. The model obtained in this study guides the managers to identify and classify the important factors of the cloud manufacturing and finally implement it successfully.

Intuitionistic fuzzy cognitive map approach for the evaluation of supply chain configuration criteria

Because of the growing global competence and effectiveness concepts, supply chain becomes more important for organizations. Therefore, managers object to find best supply chain configuration for their firms. This study proposes a comprehensive configuration for supply chain management process, and it enables to understand relationships among supply chain integration, supply chain strategies, supply chain risk factors, and performance criteria. By reviewing the literature and using experts' knowledge, supply chain configuration criteria are determined. Intuitionistic fuzzy cognitive map methodology is employed to consider the interrelations between criteria. Intuitionistic fuzzy cognitive map methodology is a suitable tool due to the presence of causalities and relationships among criteria and the difficulty of expressing the interrelations with crisp numbers. It also deals with uncertain and vague data and allows representing hesitation. The application is conducted in an automobile factory, which is one of the largest manufacturers in Turkey. The results show that selection of proper supplier is the most significant supply chain configuration criteria. Thus, the importance of supplier selection criteria is also analyzed as the second phase of the study.

Effective Brain Connectivity for fNIRS With Fuzzy Cognitive Maps in Neuroergonomics

Effective connectivity (EC) among functional near-infrared spectroscopy (fNIRS) signals is a quantitative measure of the strength of influence between brain activity associated with different regions of the brain. Evidently, accurate deciphering of EC gives further insight into the understanding of the intricately complex nature of neuronal interactions in the human brain. This article presents a novel approach to estimate EC in the human brain signals using enhanced fuzzy cognitive maps (FCMs). The proposed method presents a regularized methodology of FCMs, called effective FCMs (E-FCMs), with improved accuracy for predicting EC between real and synthetic fNIRS signals. Essentially, the revisions made in the FCM methodology include a more powerful prediction formula for FCM combined with independent tuning of the transformation function parameter. A comparison of EC in fNIRS signals obtained from E-FCM with that obtained from standard FCM, general linear model (GLM) parameters that power dynamic causal modeling (DCM), and Granger causality (GC) manifests the greater prowess of the proposed E-FCM over the aforementioned methods. For real fNIRS data, an empirical investigation is also made to gain an insight into the role of oxyhemoglobin and deoxy-hemoglobin (oxy-Hb, deoxy-Hb) in representing the cognitive activity. We believe this article has profound implications for neuroergonomics research communities.

Fuzzy cognitive maps (FCMs) for the analysis of team communication

Communication in teams plays a vital role in team success. This work proposes Fuzzy Cognitive Maps (FCMs) as a formalized, team communication methodology for the analysis of content and flow, simultaneously, of naturalistic team communication in a structured environment. Several methods of analysis of team communication exist. Few of them, however, analyze the flow and content of communication simultaneously, and none with teams using naturalistic language. Team communication data is coded for flow and content (through speech acts), then turned into FCMs for visual and statistical analysis. Results show that when using the FCM methodology both flow and content of communication can be diagrammed at the individual level, however, when assessing at the team level, what is illustrated is the flow of content. The application of different statistical analyses to the FCM output provides the opportunity to answer various questions on team communication. This study demonstrates that by using speech acts (SAs) as nodes, FCMs can illustrate and analyze team communication flow and content for naturalistic language. The FCM methodology is a powerful tool for studying team communication, and further incremental work could advance knowledge in team communication dynamics and provide contributions to graph theory. This method provides a visual overview of team communication dynamics in a naturalistic language setting, thus allowing for the study of intra-team dynamics and inter-team dynamics simultaneously.

A fuzzy scenario-based approach to analyzing neuromarketing technology evaluation factors

Neuromarketing, which uses neuroimaging techniques for marketing goals, is the application of neuroscientific methods to analyze and understand consumer behavior according to marketing initiatives. Medical diagnostic devices for brain imaging are utilized by marketers as neuromarketing technologies. This paper proposes a fuzzy scenario-based approach to analyzing neuromarketing technology evaluation criteria. Fuzzy cognitive map methodology, which is originated from the combination of fuzzy logic and neural networks, is employed due to the cause-and-effect relationships between pair of factors, presence of conflicting criteria, positive as well as negative relationships among factors, and lack of crisp data. Importance degrees of each evaluation factor are computed, and then, four different scenario analyses are provided to understand the influence of an increase or a decrease in the power of specific factor(s) on other factors. Throughout the literature, this is the first study that considers multiple and conflicting criteria framework of neuromarketing technology evaluation problem.

Building Energy Management System Modelling via State Fuzzy Cognitive Maps and Learning Algorithms

The last decades the decrease of the energy consumed by the building sector has become a crucial subject. This is the reason why the modelling and control of Building Energy Management Systems (BEMS) has become a very important scientifc feld. In such a system where the human factor and the uncertainties caused by its presence play a very important role the need for intelligent, cognitive modelling methodologies becomes obvious. The State Fuzzy Cognitive Maps Methodology combined with Nonlinear Hebbian Learning techniques are used to model the behaviour of a BEMS and improve its response.

Applying a Revised Approach of Fuzzy Cognitive Maps on a Hybrid Electrical Energy System

Complex systems modeling is a rapidly developing research field which incorporates various scientific sectors from bio medicine and energy to economic and social sciences. However, as the systems’ complexity increases pure mathematical modeling techniques prove to be a rather laborious task which demands wasting many resources and in many occasions, could not lead to the desired system response. This realization led researchers turn their attention into the field of computational intelligence; Neural Networks and Fuzzy Logic etc. In this way scientists were able to provide a model of a system which is strongly characterized by fuzziness and uncertainties. Fuzzy Cognitive Maps (FCM) in another methodology which lies in the field of computational intelligence. FCM came as a combination of Neural Networks and Fuzzy Logic and were first introduced by B. Kosko in 1986. All these years they have been applied on a variety of systems such as social, psychological, medical, agricultural, marketing, business management, energy, advertising etc, both for systems modeling and decision-making support systems, with very promising results. Classical FCM approach uses the experts’ knowledge in order to create the initial knowledge base of each system. Based on the experts’ knowledge, the interrelations among the system variables are determined and the system response is defined. Through years, improvements have been made and learning algorithms were embodied in the initial approach. Learning algorithms used data information and history to update the weights (the interconnections) among concepts (variables), contributed to the optimization of FCMs and reached more efficient systems’ response. However, all these decades, researchers have mentioned some weak points as well. In the last years substantial research has been made in order to overcome some of the well-known limitations of the FCM methodology. This paper will apply a revised approach of the Fuzzy Cognitive Maps method on a techno-economic study of an autonomous hybrid system photovoltaic and geothermal energy Specifically, the FCM model of this system includes twenty-five concepts and three of them are considered as outputs, the total system efficiency, the total energy production and the total system cost. The aim of the study is to provide maximum performance with the minimum total cost. To this end results for both the classic and revised approach of the FCM method are provided and discussed. Computational Intelligence and especially Fuzzy Cognitive Maps are a very promising field in modeling complex systems. The latest approaches of the method show that FCM can open new paths towards higher efficiency, more accurate models and effective decision-making results.

Increasing the Energy Efficiency of Buildings using Human Cognition; via Fuzzy Cognitive Maps

Abstract Fuzzy Cognitive Maps (FCMs) are a useful and powerful tool for modelling and analyzing complex dynamic systems. They can structure virtual worlds that dynamically change over time. In this paper we go beyond the classic FCM methodology by combining the state space representation and the well known FCM methodology. This new advanced method is applied to the calculation of a building’s energy consumption and the management of its load. The aim of this paper is to help improve the energy behaviour of buildings thus, contributing from a systems point of view in the effort of addressing the problem of the irrational energy consumption of the building sector. Simulations are performed as a case study, testing the new proposed method. Discussions of the obtained results along with future research directions are provided.

English

This paper investigated the implementing of soft computing methodology of fuzzy cognitive map on controlling parameters of heating, ventilating and air-conditioning systems. In the past few years, many researches have been done on application of different controllers on heating, ventilating and air-conditioning system as a more energy consuming part of the building automation system. Unlike the conventional control methods which are used more in this area like PID controller, the fuzzy cognitive maps method was chosen to control of the temperature and humidity of the room in winter operation season and summer operation season. By applying the fuzzy cognitive map controller, more energy efficiency and also more energy saving has obtained. The advantages of using fuzzy cognitive maps indicated as a controller on the typical heating, ventilating and air-conditioning system in this paper. The algorithm of FCM control reached to the goals of comfort, robustness and energy saving. Key words: Fuzzy cognitive map, heating ventilating and air-conditioning system, energy saving, energy efficiency, robustness.

Using mental-modelling to explore how irrigators in the Murray–Darling Basin make water-use decisions

Abstract Study region Water stress and over-allocation are at the forefront of water management and policy challenges in Australia, especially in the Murray–Darling Basin (MDB). Because irrigated agriculture is a major social and economic component of the MDB, farmer decision-making plays a major role in water sustainability in the region. Study focus This study used a fuzzy cognitive mapping methodology, ‘mental modeling’, to understand the perceived constraints of irrigator water-use decisions in the MDB, for two different types of irrigation: permanent and annual crops. The approach elicits and documents irrigator insights into the complex and networked nature of irrigation water use decisions in relation to farm-based dynamics. New hydrological insights for the region Results suggest support for greater local and irrigator involvement in water management decisions. Many, if not most, of the irrigators understood the need for, or at least the inevitability of, governmental policies and regulations. However, a lack of accountability, predictability, and transparency has added to the uncertainty in farm-based water decision-making. Irrigators supported the concept of environmental sustainability, although they might not always agree with how the concept is implemented. The mental modelling approach facilitated knowledge sharing among stakeholders and can be used to identify common goals. Future research utilizing the mental modelling approach may encourage co-management and knowledge partnerships between irrigators, water managers and government officials.

Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region

Coconut is one of the major perennial food crops that has a long development phase of 44 months. The climatic and seasonal variations affect all stages of coconut's long development cycle. Besides, the soil composition also plays a vital role in deciding the coconut yield behavior. The present study is focused on categorizing the coconut production level for the given set of agro-climatic conditions using the methodology of fuzzy cognitive map (FCM) enhanced by its learning capabilities. Additionally, an attempt is made to study the impact of climatic variations and weather parameters on the coconut yield behavior using the reasoning capabilities of FCM. Real coconut field data of different seasons for the period from 2009 to 2013 of Kerala state's Malabar region were used for training and evaluation of the FCM. The present work demonstrates the classification and prediction capabilities of FCM for the described precision agriculture application, with the two most known and efficient FCM learning approaches, viz., nonlinear Hebbian (NHL) and data-driven nonlinear Hebbian (DDNHL). The DDNHL-FCM offers an overall classification accuracy of 96 %. The various case studies furnished in the paper demonstrate the power of NHL-FCM in effectively reasoning new knowledge pertaining to the presented precision agriculture application.

Can fuzzy cognitive mapping help in agricultural policy design and communication?

Agricultural environmental regulation often fails to deliver the desired effects because of farmers adopting the related measures incorrectly or not at all. This is due to several barriers to the uptake of the prescribed environmentally beneficial farm management practices, most of which have been well established by social science research. Yet it is unclear why these barriers remain so difficult to overcome despite numerous and persistent attempts at the design, communication and enforcement of related agricultural policies. This paper examines the potential of fuzzy cognitive mapping (FCM) as a tool to disentangle the underlying reasons of this persistent problem. We present the FCM methodology as adapted to the application in a Scottish case study on how environmental regulation affects farmers and farming practice and what factors are important for compliance or non-compliance with this regulation. The study compares the views of two different stakeholder groups on this matter using FCM network visualizations that were validated by interviews and a workshop session. There was a farmers group representing a typical mix of Scottish farming systems and a non-farmers group, the latter comprising professionals from the fields of design, implementation, administration, consulting on and enforcement of agricultural policies. Between the two groups, the FCM process reveals a very different perception of importance and interaction of factors and strongly suggests that the problem lies in an institutional failure rather than in a simple unwillingness of farmers to obey the rules. FCM allows for a structured process of identifying areas of conflicting perceptions, but also areas where strongly differing groups of stakeholders might be able to gain common ground. In this way, FCM can help to identify anchoring points for targeted policy development and has the potential of becoming a useful tool in agricultural policy design and communication. Our results show the utility of FCM by pointing out how Scottish environmental regulation could be altered to increase compliance with the rules and where the reasons for the identified institutional failure might be sought.

Participatory Development and Analysis of a Fuzzy Cognitive Map of the Establishment of a Bio-Based Economy in the Humber Region

Fuzzy Cognitive Mapping (FCM) is a widely used participatory modelling methodology in which stakeholders collaboratively develop a ‘cognitive map’ (a weighted, directed graph), representing the perceived causal structure of their system. This can be directly transformed by a workshop facilitator into simple mathematical models to be interrogated by participants by the end of the session. Such simple models provide thinking tools which can be used for discussion and exploration of complex issues, as well as sense checking the implications of suggested causal links. They increase stakeholder motivation and understanding of whole systems approaches, but cannot be separated from an intersubjective participatory context. Standard FCM methodologies make simplifying assumptions, which may strongly influence results, presenting particular challenges and opportunities. We report on a participatory process, involving local companies and organisations, focussing on the development of a bio-based economy in the Humber region. The initial cognitive map generated consisted of factors considered key for the development of the regional bio-based economy and their directional, weighted, causal interconnections. A verification and scenario generation procedure, to check the structure of the map and suggest modifications, was carried out with a second session. Participants agreed on updates to the original map and described two alternate potential causal structures. In a novel analysis all map structures were tested using two standard methodologies usually used independently: linear and sigmoidal FCMs, demonstrating some significantly different results alongside some broad similarities. We suggest a development of FCM methodology involving a sensitivity analysis with different mappings and discuss the use of this technique in the context of our case study. Using the results and analysis of our process, we discuss the limitations and benefits of the FCM methodology in this case and in general. We conclude by proposing an extended FCM methodology, including multiple functional mappings within one participant-constructed graph.

Identification of cryovolcanism on Titan using fuzzy cognitive maps

Future planetary exploration of Titan will require higher degrees of on-board automation, including autonomous determination of sites where the probability of significant scientific findings is the highest. In this paper, a novel Artificial Intelligence (AI) method for the identification and interpretation of sites that yield the highest potential of cryovolcanic activity is presented. We introduce the theory of fuzzy cognitive maps (FCM) as a tool for the analysis of remotely collected data in planetary exploration. A cognitive model embedded in a fuzzy logic framework is constructed via the synergistic interaction of planetary scientists and AI experts. As an application example, we show how FCM can be employed to solve the challenging problem of recognizing cryovolcanism from Synthetic Aperture Radar (SAR) Cassini data. The fuzzy cognitive map is constructed using what is currently known about cryovolcanism on Titan and relies on geological mapping performed by planetary scientists to interpret different locales as cryovolcanic in nature. The system is not conceived to replace the human scientific interpretation, but to enhance the scientists’ ability to deal with large amounts of data, and it is a first step in designing AI systems that will be able, in the future, to autonomously make decisions in situations where human analysis and interpretation is not readily available or could not be sufficiently timely. The proposed FCM is tested on Cassini radar data to show the effectiveness of the system in reaching conclusions put forward by human experts and published in the literature. Four tests are performed using the Ta SAR image (October 2004 fly-by). Two regions (i.e. Ganesa Macula and the lobate high backscattering region East of Ganesa) are interpreted by the designed FCM as exhibiting cryovolcanism in agreement with the initial interpretation of the regions by Stofan et al. (2006). Importantly, the proposed FCM is shown to be flexible and adaptive as new data and knowledge are acquired during the course of exploration. Subsequently, the FCM has been modified to include topographic information derived from SAR stereo data. With this additional information, the map concludes that Ganesa Macula is not a cryovolcanic region. In conclusion, the FCM methodology is shown to be a critical and powerful component of future autonomous robotic spacecraft (e.g., orbiter(s), balloon(s), surface/lake lander(s), rover(s)) that will be deployed for the exploration of Titan.