Fuzzy Cognitive Map Model Research Articles

PREDICTION OF SEED PURITY AND VARIETY IDENTIFICATION USING IMAGE MINING TECHNIQUES

Seed is a little embryonic plant that can be used to introduce plant infections to new areas while also allowing them to survive from one cropping season to the next. Seed health is a well-known component in modern agricultural science for achieving the required plant population and yield. Seed-borne fungus are a major biotic constraint in seed production around the world. The detection of seed-borne pathogens by seed health testing is a crucial step in the treatment of crop diseases. Speed and accuracy are critical requirements for longterm economic growth, competitiveness, and sustainability in agricultural output. Because human judgements in identifying objects and situations are variable, subjective, and delayed, seed prediction activities are costly and unreliable. Machine vision technology provides a nondestructive, cost-effective, quick, and accurate option for automated procedures. Seed variety, seed type (country seed or hybrid seed), seed health, and purity prediction were the four basic processes. We began the first procedure by aligning the seed bodies in the same direction using a seed orientation approach. Then, to detect atypical physical seed samples, a quality screening procedure was used. Their physical characteristics, such as shape, colour, and texture, were retrieved to serve as data representations for the prediction. This research introduces a new fuzzy cognitive map (FCM) model based on deep learning neural networks that predicts seed purity tests using data from biological investigations. The relevant data features from the seed test are extracted by FCM, which then effectively initialises the deep neural networks. The Levenberg–Marquardt (LM) technique for deep neural networks was discovered to improve seed purity test prediction. Four statistical machine learning algorithms (BP-ANN, Multivariate regression, and FCMLM deep learning). Furthermore, we demonstrated an improvement in the system''''s overall performance in terms of data quality, including seed orientation and quality screening. In independent numerical testing, the correlation coefficient between predicted values and true values acquired from experiments reached 0.9.

Supply chain operation evaluation and management decision by fuzzy cognitive map model

AbstractThe components of the logistics and supply chain are analysed to meet the needs of supply chain management and decision support, aiming at the complexity and uncertainty of the supply chain system. First, the characteristics of logistics demand and logistics service types of some enterprises are analysed. The factors affecting supplier selection are obtained through the questionnaire survey of some enterprises. Second, the mutual feedback relationship between the influencing factors of logistics service demand is analysed, and a fuzzy cognitive graph model of the selection process of enterprise logistics service providers is established. Finally, the fuzzy cognitive map (FCM) model is used to conduct an empirical study on the choice of logistics service providers. The results show that the ranking results of the three candidate logistics service companies based on FCM are WL1 (0.7233) > WL3 (0.6823) > WL2 (0.6764). When each node of the concept does not affect each other, the ranking result of the three candidate logistics service companies based on analytic hierarchy process (AHP) is WL1 (0.7265) > WL2 (0.6949) > WL3 (0.6920). The final evaluation result will be affected by the mutual influence relationship between the concept nodes. Overall, the lowest evaluation score is WL2, and the highest is WL1. The most suitable logistics service provider for the enterprise is WL1. The FCM model selected by the manufacturing enterprise logistics service provider is reasonable and feasible. The mutual feedback between evaluation factors affects the selection of logistics service providers in a company. The FCM can get a more accurate evaluation weight value and can realize the optimal choice of logistics suppliers. It provides reference and decision support for other enterprises.

Neural network based fuzzy cognitive map

A Fuzzy Cognitive Map (FCM) is a powerful technique for modeling and analyzing complex systems. In this study, we propose a novel learning algorithm that, unlike existing FCM-based learning algorithms, ensures matching the desired system state by computing the otherwise “unexplained” biases in the model. Our learning algorithm considers both the whole system bias and the individual biases for each system factor (concept). We explore the impact of FCM structure and characteristics for the proposed algorithm and suggest an interpretation of computed biases. Finally, we propose an FCM visualization technique which enables comparison between and a deeper understanding of modeled systems. As FCMs offer a broader, quantifiable view of the causal relationships between factors, the approach used in this study provides insights into FCM modeling and application to real-world complex systems.

Analysis of University Student Cadres' Retreat Actions in China Based on K-Core Algorithm and FCM Model

In recent years, there have been constant disputes over college student cadres. Starting from the public opinion crisis of College Students’ autonomy and using the “K-core” algorithm, this paper found that the college student cadres could not realize their own correct roles. The fuzzy cognitive map (FCM) model was used to analyze 49 influencing factors of N University's retreat action. It is found that the five factors of mass support, ability, ideology and morality, work tasks, and performance evaluation are important factors leading to the retreat action. This research proposes role reconstruction and system rectification strategies for the five variables in order to achieve a true feeling of student autonomy and good governance.

Digital Marketing Enhancement of Cryptocurrency Websites through Customer Innovative Data Process

Today, more than ever, the popularity of decentralized payment systems has risen, creating an outbreak of new cryptocurrencies hitting the market. Unique websites have been staged for each cryptocurrency, where information and means for mining cryptocurrencies are available daily. People visit those cryptocurrency websites either from desktop or mobile devices. Thus, the impulsion for appropriate promotion of cryptocurrency websites and customer factors affecting it rises. The above process increases cryptocurrency organizations’ website visibility, raising the need for customer relationships and satisfaction optimization concerning organizations’ supply chain strategy. Research data were collected from 10 well-known cryptocurrency websites, regarding mobile and desktop devices, in 180 days, regarding on-site web analytics. Therefore, a model consisting of three stages was applied. Starting phase of the model pertains to statistical and regression analysis of cryptocurrency web analytics, followed by Fuzzy Cognitive Mapping and Agent-Based Model deployment. Throughout this study, methods for promoting cryptocurrency websites can be deduced from assessing specific website metrics and device preferences. Research results indicate that web analytics give a clearer image of customer behavior in cryptocurrency websites and, therefore, provide opportunities for further website optimization through increased web traffic and digital reputation.

Research on policy diffusion mechanism of Government Procurement of Public Services based on an MFCM

PurposeThe purpose of this paper is to show that research on policy diffusion mechanism of Government Procurement of Public Services (GPPS) is beneficial to improve the efficiency of policy formulation and implementation.Design/methodology/approachIn view of the four dimensions which are internal demand, external pressure, policy innovation environment and service characteristic, a system of factors affecting policy diffusion is established. On this basis, a Multilayer Fuzzy Cognitive Map (MFCM) model for policy diffusion of GPPS is constructed. Nonlinear Hebbian Learning algorithm and genetic algorithm are applied to optimize the two components of the MFCM model, which are relationship between nodes at the same layer and influence weights between nodes at different layers, respectively. Taking Nanjing municipal government purchasing elderly-care services in China as the empirical object, simulation of policy diffusion based on the MFCM model is carried out, aiming to obtain the key factors influencing policy diffusion and the dynamic diffusion mechanism of GPPS policy.FindingsResearch results show that, compared with monolayer Fuzzy Cognitive Map, the MFCM model converges faster. In addition, simulation results of policy diffusion indicate that economic development level of jurisdiction, superior pressure, administrative level and operability of services are key influencing factors which are under four dimensions correspondingly. And the dynamic influencing mechanism of key factors has also been learned.Originality/valueThis paper constructs the MFCM model, which is a new approach based on several monolayer FCMs, to study the policy diffusion mechanism.

An interpretable prediction of FCM driven by small samples for energy analysis based on air quality prediction

ABSTRACT In order to achieve prevention and control of air pollution through energy consumption adjustment in advance, the paper proposes an Fuzzy Cognitive Map (FCM) of various energy resources affecting air quality, an incremental prediction algorithm of FCM and gradient descending method used to learn the FCM based on the small sample data on various energy consumptions and concentration of air pollutants. The FCM as an interpretable prediction method not only can predict future air quality more accurately, but also can analyze and interpret the affecting of various energy types on the future air quality. As the time delay of various energy consumptions affecting concentration of air pollutants, the quantitative time sequence influencing relationships (causality) in the FCM is mined directly from these data, and the air quality affected by various types of energy consumptions is predicted based on the FCM. Accordingly, the energy types affecting air pollution can be obtained for prior decision of energy consumption structure adjustment. The experimental results in Beijing-Tianjin-Hebei show that the FCM modeling is better than Support Vector Regression (SVR), Linear Regression (LR), Principal Component Analysis (PCA)-based forecasting, Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) methods in predicting air quality affected by energy resources, meanwhile according to the interpretable prediction results of the FCM, we obtain some interesting results and suggestions on energy consumption types in Beijing-Tianjin-Hebei regions in advance. Implications: At present, China’s air pollution control has entered the deep-water area, and the biggest challenge is how to adjust the energy (consumption) structure. Therefore, this study completed the two important tasks: (1) driven by small sample data of energy consumptions, the paper provides an interpretable prediction model and method with better performance to achieve prevention and control of air pollution through energy consumption adjustment in advance; (2) according to the interpretable prediction results, the paper obtains some interesting results used to guide energy consumption adjustment in Beijing-Tianjin-Hebei regions. This study will provide beneficial suggestions and strategies for air pollution prevention and control in Beijing-Tianjin-Hebei, will help improve the air quality and energy consumption structure in Beijing-Tianjin-Hebei, and also can be extended to other regions.

COVID-19 and Sustainable Development Goals (SDGs): Scenario analysis through fuzzy cognitive map modeling

The COVID-19 crisis has immensely impacted the implementation of the 2030 Agenda for Sustainable Development worldwide. This research aims at providing a policy response to support achieving the Sustainable Development Goals (SDGs) taking the COVID-19 long-term implications into account. To do so, a qualitative analytical method was employed in the following four steps. First, a fuzzy cognitive map was developed to specify causal-effect links of the interdependent SDGs in Iran as a developing country in the Middle East. Second, potential effects of the pandemic on the SDGs achievement were analyzed. Third, five strategies were formulated, including green management, sustainable food systems, energizing the labor market, inclusive education, and supporting research and technology initiatives in the energy sector. And finally, different scenarios corresponding to the five proposed strategies were tested based on the identified interconnections among the SDGs. The analysis showed that applying each of the five considered strategies or their combination would mitigate the effect of COVID-19 on the SDGs only in case of a medium pandemic activation level. Moreover, implementing a single strategy with a high activation level leads to better outcomes on the SDGs rather than applying a combination of strategies in low or medium activation levels during the pandemic situation. The provided insights support stakeholders and policy-makers involved in the post-COVID-19 recovery action plan towards implementing the 2030 Agenda for Sustainable Development.

An analytical approach for evaluating the impact of blockchain technology on sustainable supply chain performance

Nowadays, distributed ledger technologies (e.g., blockchain technology) can play significant roles in improving supply chain management and sustainability. Blockchain technology can support responsible sourcing and ensure compliance with environmental standards by boosting traceability and transparency in sustainable supply chains. Nevertheless, blockchain technology has not been widely applied in this field due to the lack of familiarity of managers with its intrinsic characteristics. This study investigates the performance improvement, arising from the blockchain implementation, to address the problem of managerial conservatism and this technology adoption. Accordingly, an analytical approach is proposed to identify blockchain technology adoption enablers and analyze their impact on supply chain performance. At first, the main enablers, derived from the literature review, are explored using network theory. The existing causal relationships between enablers are extracted based on a multi-expertise team members’ agreement. Then, the fuzzy inference system is employed to determine the weights of relationships between the identified enablers and supply chain performance-related targets. After modeling the extracted causal relationships using the fuzzy cognitive map model, a scenario is defined for each enabler, and its impact on improving supply chain performance is estimated by implementing the hybrid learning algorithm. Finally, the obtained outputs are used to prioritize the blockchain technology adoption enablers using the fuzzy data envelopment analysis model. The result of this study implies that the blockchain technology can have a significant impact on mineral supply chain performance by creating smart contracts and enhancing environmental sustainability, traceability, and transparency.

Modeling implicit bias with fuzzy cognitive maps

This paper presents a Fuzzy Cognitive Map model to quantify implicit bias in structured datasets where features can be numeric or discrete. In our proposal, problem features are mapped to neural concepts that are initially activated by experts when running what-if simulations, whereas weights connecting the neural concepts represent absolute correlation/association patterns between features. In addition, we introduce a new reasoning mechanism equipped with a normalization-like transfer function that prevents neurons from saturating. Another advantage of this new reasoning mechanism is that it can easily be controlled by regulating nonlinearity when updating neurons’ activation values in each iteration. Finally, we study the convergence of our model and derive analytical conditions concerning the existence and unicity of fixed-point attractors.

Introducing Fuzzy Cognitive Map for predicting Engine’s Health Status

Predictive maintenance (PdM) is an open issue in industrial and manufacturing systems. Remaining Useful Life (RUL) estimation is a significant concern in PdM, which corresponds to the engine’s remaining time to perform its intended function. This work proposes Fuzzy Cognitive Maps (FCMs) as a Health Indicator (HI) prognostics method for engines’ RUL prediction. For the FCM model design, we apply, test, and compare four different training algorithms to determine the model parameters’ values that lead to the optimal prediction accuracy. In order to test the results obtained from the FCM-based approach, we compare them with those derived from an Artificial Neural Network (ANN), and we verify that the use of FCMs results in a decent engine’s health status predictor comparable to a machine learning method.

Constraint reformulations for set point optimization problems using fuzzy cognitive map models

AbstractThe selection of optimal set points is an important problem in modern process control. Fuzzy cognitive maps (FCMs) allow to construct models of complex processes using expert knowledge, which is particularly useful in situations where measuring the variables of interest online is problematic. These models can be used as constraints in optimization problems with the objective of determining optimal set points for those processes. This article presents a reformulation of the constraints imposed by the FCM models that reduces the complexity of the resulting optimization problem and enables the application of heuristic methods for its solution. Computational results show that the use of separable programming on the reformulated problem constitutes a very good alternative, both in terms of solution time and reliability in finding the optimum, enabling the application of FCM modeling to larger systems and easing the practical implementation of the approach.

Global stability of fuzzy cognitive maps

Complex systems can be effectively modelled by fuzzy cognitive maps. Fuzzy cognitive maps (FCMs) are network-based models, where the connections in the network represent causal relations. The conclusion about the system is based on the limit of the iteratively applied updating process. This iteration may or may not reach an equilibrium state (fixed point). Moreover, if the model is globally asymptotically stable, then this fixed point is unique and the iteration converges to this point from every initial state. There are some FCM models, where global stability is the required property, but in many FCM applications, the preferred scenario is not global stability, but multiple fixed points. Global stability bounds are useful in both cases: they may give a hint about which parameter set should be preferred or avoided. In this article, we present novel conditions for the global asymptotical stability of FCMs, i.e. conditions under which the iteration leads to the same point from every initial vector. Furthermore, we show that the results presented here outperform the results known from the current literature.

Modeling time series by aggregating multiple fuzzy cognitive maps.

BackgroundThe real time series is affected by various combinations of influences, consequently, it has a variety of variation modality. It is hard to reflect the variation characteristic of the time series accurately when simulating time series only by a single model. Most of the existing methods focused on numerical prediction of time series. Also, the forecast uncertainty of time series is resolved by the interval prediction. However, few researches focus on making the model interpretable and easily comprehended by humans.MethodsTo overcome this limitation, a new prediction modelling methodology based on fuzzy cognitive maps is proposed. The bootstrap method is adopted to select multiple sub-sequences at first. As a result, the variation modality are contained in these sub-sequences. Then, the fuzzy cognitive maps are constructed in terms of these sub-sequences, respectively. Furthermore, these fuzzy cognitive maps models are merged by means of granular computing. The established model not only performs well in numerical and interval predictions but also has better interpretability.ResultsExperimental studies involving both synthetic and real-life datasets demonstrate the usefulness and satisfactory efficiency of the proposed approach.

Применение когнитивного моделирования для обоснования выбора бизнес-модели экономической организации

This article considers the urgent scientific and practical task of developing analytical tools to justify the choice and transformation of a business model for an economic organization in a rapidly changing business environment. The mathematical apparatus of cognitive modelling is proposed to use to solve this problem, which makes it possible, based on the analysis of cause- effect relationships between the most significant conditions and factors affecting the economic efficiency and competitiveness of doing business, to improve the reasonableness and reduce the subjectivity of the decisions made by its owners and top managers. An algorithm to justify the choice of a business model for an organization based on the construction and analysis of fuzzy cognitive maps (FCM) of business models is proposed. A common list of business factors (concepts) and a set of causality relations between each pair of concepts has been defined, and a basic FCM of the business model is developed as the basis for constructing FCM models of specific businesses. An example of the application of the developed toolkit in a strategic analysis of the conditions and factors of an organization undertaking the production of furniture, carried out to justify a change in competitive strategy and the associated transformation of the business model, is considered. The analysis has been performed using specialized cognitive modelling software developed at the department of information technologies in economics and management, branch of the National Research University Moscow Power Engineering Institute in Smolensk. The proposed toolkit improves methods and algorithms for intelligent decision-making support in the management of economic and social objects and can find practical application in the strategic management of commercial organizations in various fields, as well as be used by professional consultants in the preparation of recommendations for business model change.

Modeling the decision-making process of financial path transition from entrepreneur's perspective using a fuzzy cognitive mapping

PurposeThis study explores how entrepreneurs modify their financial path(s) and go beyond job security to attain greater financial freedom. The present work examines the cash-flow quadrant (CFQ) attributes and demonstrates the importance of the push-pull factors for an individual's quadrant transition in achieving financial freedom.Design/methodology/approachA hypothetical model and an abductive approach were used through regression models in a population sample of 260 Bahraini entrepreneurs. Fuzzy participatory cognitive mapping was also used to develop a conceptual model of financial path transition's decision making among entrepreneurs and study the impact of certain push-pull factors on the entrepreneurs' decisions.FindingsThe triangulated study identifies six categories of variables: financial freedom, workplace condition, independence, salary level, family life-building and retirement savings as key pull-push factors that significantly impact financial path transition's decision. Fuzzy cognitive mapping (FCM) extends our knowledge of the dynamics of CFQ transitions from a push-pull factor perspective. The results indicate no significant differences between the variables listed in the regression model and the fuzzy cognitive map model. Four categories of pull-push factors appeared as the entrepreneurs' top rankings when ordered by complexity, centrality scores and impact weight. These categories were workplace conditions, financial freedom, independence and salary level. The findings widen the scope of knowledge of each quadrant and rationalize how and why such factors impact quadrant decisions among Bahraini entrepreneurs.Originality/valueMany studies discuss the CFQ model and consider its quadrants a specific method for identifying a unique financial path to generate income. A shifting quadrant occurs when individuals want to change their financial path and move beyond job security to achieve more financial freedom. Although this transition is well-established in the literature, the factors accounting for the individual's transition across quadrants have not received enough attention. This study fills this gap and calls for more in-depth investigations of this area to better understand the dynamics of CFQ transitions from a push-pull factor perspective.

Structured sparsity learning for large-scale fuzzy cognitive maps

Fuzzy cognitive map (FCM) as a kind of intelligent soft computing method, by combining the advantages of neural network and fuzzy logic, can be used to mine the causal relationships between concepts and make reasoning. However, how to effectively learn the large-scale FCMs is still an open problem. In this article, by means of structured sparsity learning, a robust learning method for large-scale FCMs based on iterative smoothing algorithm is proposed. Firstly, in terms of sparse signal reconstruction, the objective function of learning method is constructed by using elastic and total variation (TV) penalties, which can be conducive to capture the sparse structure information of FCM and improve the robustness of network reconstruction. Due to the non-smoothness of the TV penalty, Nesterov’s smoothing technique is used to solve the non-smooth problem, thus transforming the problem into a convex optimization problem. Subsequently, in order to quickly solve the convex optimization, the algorithm based on proximal gradient descent is applied. In the experiment part, synthetic FCM models with different densities, sizes and noises are used to evaluate the proposed method, and the experimental results demonstrate the proposed method can make full use of the observations to learn the structural information of FCM. Moreover, the real-world data from the gene regulatory networks (GRNs) are further used to evaluate the effect of network reconstruction, and a higher reconstruction accuracy can be verified.

A study of dynamic fuzzy cognitive map model with group consensus based on linguistic variables

A fuzzy cognitive map (FCM) is an analysis tool that uses a graph structure to show the causal relationships of influence factors in a decision-making system. During the decision-making process, it is reasonable for experts to use linguistic variables to express their subjective opinions. However, few studies have discussed methods for aggregating the linguistic opinions of experts to reach a group consensus in FCM. In addition, the interaction weights among the factors in FCM will usually change over time in a real environment. Therefore, we applied the learning algorithm to adjust the interaction weights among the factors in the steps of FCM, after which we proposed a dynamic fuzzy cognitive map model with group consensus based on the linguistic evaluations in this study. Finally, we presented a case study using the proposed model to illustrate the development possibility of the Internet of Things (IoT) industry in Taiwan. According to the analysis results, we found that the development prospects for the IoT industry in Taiwan are optimistic. The four key factors for IoT industry development were found to be the degree of authorization and trust, the development of application technologies, the complexity of systems and equipment and cross-platform possibility.

Socio-cognitive constraints and opportunities for sustainable intensification in South Asia: insights from fuzzy cognitive mapping in coastal Bangladesh

Appreciating and dealing with the plurality of farmers’ perceptions and their contextual knowledge and perspectives of the functioning and performance of their agroecosystems—in other words, their ‘mental models’—is central for appropriate and sustainable agricultural development. In this respect, the sustainable development goals (SDGs) aim to eradicate poverty and food insecurity by 2030 by envisioning social inclusivity that incorporates the preferences and knowledge of key stakeholders, including farmers. Agricultural development interventions and policies directed at sustainable intensification (SI), however, do not sufficiently account for farmers’ perceptions, beliefs, priorities, or interests. Considering two contrasting agroecological systems in coastal Bangladesh, we used a fuzzy cognitive mapping (FCM)-based simulation and sensitivity analysis of mental models of respondents of different farm types from 240 farm households. The employed FCM mental models were able to (1) capture farmers’ perception of farming system concepts and relationships for each farm type and (2) assess the impact of external interventions (drivers) on cropping intensification and food security. We decomposed the FCM models’ variance into the first-order sensitivity index (SVI) and total sensitivity index (TSI) using a winding stairs algorithm. Both within and outside polder areas, the highest TSIs (35–68%) were observed for effects of agricultural extension on changes in other concepts in the map, particularly food security and income (SI indicators), indicating the importance of extension programs for SI. Outside polders, drainage and micro-credit were also influential; within polders, the availability of micro-credit appears to affect farmer perceptions of SI indicators more than drainage. This study demonstrated the importance of reflection on the differing perspectives of farmers both within and outside polders to identify entry points for development interventions. In addition, the study underscores the need for micro-farming systems-level research to assess the context-based feasibility of introduced interventions as perceived by farmers of different farm types.

The Modeling of Time Series Based on Least Square Fuzzy Cognitive Map

A novel design method for time series modeling and prediction with fuzzy cognitive maps (FCM) is proposed in this paper. The developed model exploits the least square method to learn the weight matrix of FCM derived from the given historical data of time series. A fuzzy c-means clustering algorithm is used to construct the concepts of the FCM. Compared with the traditional FCM, the least square fuzzy cognitive map (LSFCM) is a direct solution procedure without iterative calculations. LSFCM model is a straightforward, robust and rapid learning method, owing to its reliable and efficient. In addition, the structure of the LSFCM can be further optimized with refinements the position of the concepts for the higher prediction precision, in which the evolutionary optimization algorithm is used to find the optimal concepts. Withal, we discussed in detail the number of concepts and the parameters of activation function on the impact of FCM models. The publicly available time series data sets with different statistical characteristics coming from different areas are applied to evaluate the proposed modeling approach. The obtained results clearly show the effectiveness of the approach.