Adaptive learning of fuzzy cognitive maps

PurposeThe purpose of this paper is to apply a fuzzy LinPreRa cognitive map to evaluate the interaction and importance of factors affecting the development of strategic alliance partnerships between the outlying island duty-free shops and existing collaboration firms in duty-free shops. Meanwhile, the key factors should be considered in establishing strategic alliance partnerships while analyzing and comparing the perspectives of owners for outlying island duty-free shop and partner vendors of collaborating firms along with differences of influencing key factors on partnership quality.Design/methodology/approachThis study incorporates a fuzzy linguistic preference relation analytical network process (fuzzy LinPreRa ANP) in the fuzzy cognitive map (FCM) method to formulate a fuzzy LinPreRa cognitive map to evaluate the interactions and importance of key factors and the conditions of interactive impacts during the establishment of strategic alliance partnerships. The authors use the outlying island duty-free shops in Taiwan as the empirical subject to illustrate how the fuzzy LinPreRa cognitive map is applied. In-depth, interviews and questionnaire surveys are conducted to collect and evaluate respondents concerning key factors affecting strategic alliance partnerships establishment.FindingsThe following three findings based on the results of empirical analysis: first, the administrative behavioral patterns of managers for strategic alliance partnerships encompass shared values and goal coherence, while the associative statements are located on the first layer of fuzzy LinPreRa cognitive map core associations, which illustrates that businesses attach great importance to conceptual ideas. Second, integrity and reputations of both parties are the governing mechanism of strategic alliance partnerships, influencing mutual reputation. Third, the relationship of strategic alliance partnerships refers to the profit opportunities of both parties and their ability to respond to the market, including future development, regional indicators, marketing capabilities, brand multiplicity and customer retention. However, it can be inferred that such associative factors are located in the outer layer or belong to noncore associations, which means that both parties’ abilities to respond to market reactions are weakened.Practical implicationsThis study provides valuable relationship managerial strategies to maintain long-term partnerships for outlying island duty-free shops and their alliance collaborating firms including strengthened relationships of both parties’ managers to achieve common values and consistent objectives; improved beneficial value of both parties in strategic alliance partnerships; continued close communications to enhance the quality of strategic alliance partnerships; and establishment of personnel training mechanisms and strict formulation of management rules for strategic alliance partnerships.Originality/valueThe main valuable contributions are included the fuzzy LinPreRa cognitive map by combining two different decision methods including FCM and fuzzy LinPreRa ANP is proposed to help decision makers to improve the evaluation quality and calculation efficiency for critical elements’ interaction and importance; the fuzzy LinPreRa cognitive map can clarify considering significant factors when maintaining strategic alliance partnerships and further provide valuable relationship managerial strategies to maintain long-term relationships for duty-free shop owners and their alliance collaborating firms.

Celiac disease (CD) is a complex disorder whose development is affected by genetics (HLA alleles) and gluten ingestion. Its diagnosis is very difficult due to clinical manifestations complexity, latent period, and similarity to other diseases. Studies show that a high percentage of CD patients remain undiagnosed. The celiac patients who are not treated are at a high risk of cancer, malignant lymphoma, and small-bowel neoplasia. Therefore, CD diagnosis and grading is of paramount importance. This paper presents a new method for grading CD based on the combination of fuzzy cognitive map (FCM) and support vector machine. To improve the efficiency and increase classification ability of FCM, particle swarm optimization (PSO) algorithm is applied to adjust FCM weights. In this study, the newest method of grading A, B1, and B2 is used. The empirical results show that the main advantage of PSO algorithm is its speed of convergence and the ability to obtain faster possible schedules. The proposed method is tested on 89 patients. The simulation results prove the superiority of the proposed method compared with Bayesian networks based on the rules and other procedures set forth in the literature. These results show the percentages of 87%, 86%, and 84% for three grades of A, B1 and B2.

Fuzzy Cognitive Maps (FCMs) are a causal modelling technique. FCM models contain nodes (representing the concepts to be modelled) and directed weighted edges (representing the causal relations between the concepts). Data-driven FCM learning algorithms are an objective approach with the potential to discover the causal relations that are unknown to human experts. Learning FCM from data can be a difficult problem because the size of the solution space grows quadratically with the number of nodes in the FCM models. A data-driven learning algorithm based on Ant Colony Optimization (ACO) is proposed to develop Fuzzy Cognitive Maps (FCMs). The FCM models can be isomorphically represented as weight vectors. The objective function is to minimize the difference between the estimated response of the FCM model and the target response observed from the to-be-modelled system. An ACO algorithm with heuristic information is proposed to find the best FCM model. The performance of the ACO algorithm was tested on both randomly generated data and DREAM4 project data (publicly available in-silico gene expression data). The experiment results show that the ACO algorithm is able to learn FCMs with at least 40 nodes.

Fuzzy cognitive maps (FCMs) are generally applied to model and analyze complex dynamical systems. Recently, many evolutionary-based algorithms are proposed to learn FCMs from historical data by optimizing Data_Error, which is used to evaluate the difference between available response sequences and generated response sequences. However, when Data_Error is adopted as the fitness function for learning FCMs, two problems arise. One is that the optimization reaches the desired result slowly; the other is that the learned FCMs have high link density. To solve these problems, we propose another objective named as convergence error, which is inspired by the convergence of FCMs, to evaluate the difference between the convergent value of available response sequences and that of generated response sequences. In addition, a multi-agent genetic algorithm (MAGA), which is effective for large-scale global numerical optimization, is adopted to optimize convergence error for learning FCMs. To this end, a novel learning approach, a multi-agent genetic algorithm based on the convergence error (MAGA-Convergence), is proposed for learning FCMs. MAGA-Convergence needs less data, because the only initial value and convergent value of the available response sequences are needed for learning FCMs. In the experiments, MAGA-Convergence is applied to learn the FCMs of synthetic data and the benchmarks DREAM3 and DREAM4 for gene regulatory network reconstruction. The experimental results show that the learned FCMs are sparse and could be learned in much fewer generations than other learning algorithms.

Fuzzy cognitive maps (FCMs) are cognition fuzzy influence graphs, which are based on fuzzy logic and neural networks. Many automated learning algorithms have been proposed to reconstruct FCMs from data, but most learned maps using such methods are much denser than those constructed by human experts. To this end, we first model the FCM learning problem as a multiobjective optimization problem, and then propose a multiobjective evolutionary algorithm, labeled as MOEA-FCM, to learn FCM models. MOEA-FCM is able to learn FCMs with varying densities at the same time from input historical data, which can provide candidate solutions with different properties for decision makers. In the experiments, the performance of MOEA-FCM is validated on both synthetic and real data with varying sizes and densities. The experimental results demonstrate the efficiency of MOEA-FCM and show that MOEA-FCM can not only reconstruct FCMs with high accuracy without expert knowledge, but also create a diverse Pareto optimal front which consists of FCMs with varying densities. The significance of this study is that decision makers can choose different FCM models provided by MOEA-FCM based on their practical requirements.

Fuzzy cognitive maps (FCMs) are a class of discrete-time artificial neural networks that are used to model dynamic systems. A recently introduced supervised learning method, which is based on real-coded genetic algorithm (RCGA), allows learning high-quality FCMs from historical data. The current bottleneck of this learning method is its scalability, which originates from large continuous search space (of quadratic size with respect to the size of the FCM) and computational complexity of genetic optimization. To this end, the goal of this paper is to explore parallel nature of genetic algorithms to alleviate the scalability problem. We use the global single-population master-slave parallelization method to speed up the FCMs learning method. We investigate the influence of different hardware architectures on the computational time of the learning method by executing a wide range of synthetic and real-life benchmarking tests. We analyze the quality of the proposed parallel learning method in application to both dense and sparse large FCMs, i.e. maps that consist of several dozens of concepts. The parallelization is shown to provide substantial speed-ups, allowing doubling the size of the FCM that can be learned by parallelization with 8 processors.

The learning algorithm of fuzzy cognitive map(FCM) is mainly based on expert intervention or effective historical data to learn the weight value in weight matrix W. With the increase of the number of nodes, the number of weights need to be learned will also increase square, the performance of the learning algorithm will decline sharply. Therefore, it is an important task to design a high-performance learning algorithm which depends on expert knowledge as little as possible. A single learning algorithm has some limitations, such as easy local optimization, slow learning rate and so on. Among them, particle swarm optimization algorithm has strong global optimization ability, which can quickly optimize the weight matrix to a certain accuracy, but the follow-up optimization time is too long. For the gradient descent algorithm, when the initial value is chosen properly, its can approach the global optimal solution quickly. In this paper, we design particle swarm optimization (PSO) and gradient descent algorithm (GDA) to learn FCM respectively. According to the characteristics of the two algorithms, a hybrid algorithm(HA) is proposed: first, the PSO algorithm is used to optimize weight matrix to a certain accuracy, then the gradient descent method is added, which makes up for the disadvantage that the error of single PSO algorithm decreases slowly in the later stage. Experimental results show that the hybrid algorithm proposed in this paper has a significant improvement in both accuracy and speed.

Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture application

Fuzzy Cognitive Maps (FCMs), first introduced by Kosko, are graph-based knowledge representation tools. In order to improve the efficiency, robustness and accuracy of FCMs, different learning approaches and algorithms have been introduced in the literature. The algorithms aim to revise the initial knowledge of experts and/or extract useful knowledge from historical records in order to yield learned weights. One considerable drawback of FCM is that, in its original form, it often yields the same output under different initial conditions. Since the results of the learning algorithms are highly dependent on the reasoning mechanism (i.e. updating function) of FCMs, this drawback also affects the performance and accuracy of these algorithms. Therefore, problems including (conflicting) multiple initial vectors, multiple weight matrices and multiple desired final state vectors have received only limited attention. In order to address this issue and provide a better modeling framework for this type of problems, a compromise-based new fuzzy cognitive mapping approach based on particle swarm optimization is suggested. To justify the effectiveness and applicability of the proposed approach, an illustrative example is provided.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于模糊认知图的生态风险管理探究 DOI: 10.5846/stxb201301280173 作者: 作者单位: 山西大学黄土高原研究所,山西大学环境与资源学院,山西大学环境与资源学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41101558，41271513）；山西省研究生创新项目（020752901007） Exploration of ecosystem risk management based on fuzzy cognitive map Author: Affiliation: Institute of Loess Plateau,Shanxi University,Department of environment and resources,Shanxi University,Department of environment and resources,Shanxi University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:生态风险管理具有不稳定性和不确定性，如何获取和调整不同利益群体对于生态风险和环境管理的看法并把它们定量表示出来、协调其中存在的冲突是生态系统管理亟待解决的问题。以煤矿区生态系统为研究对象，探讨了模糊认知图方法在获取利益相关方认知上的应用，并基于模糊关联矩阵，采用神经网络模型进行不同生态风险管理政策的情景模拟。研究结果为：大气污染和水环境破坏是所有利益相关者最关注的问题，国家政策是最核心的变量；不同利益相关者之间的不同点在于，管理者较多地关注国家政策及煤矿开采给国家带来的财政收入与居民生活水平的提高，而当地居民和矿工更关注自己的身体健康和人身安全。通过对煤矿区生态风险管理的政策模拟，可看出要减少大气污染，政府要加大对企业的治理力度，从保护耕地、林地，增加植被覆盖度，提高生物多样性出发；另外，如果政府制定相关政策提高了次级产物的利用率，矿区生态系统的风险也会大大降低。结果表明：模糊认知图方法用在生态系统管理中，可以真实反映利益相关者对生态系统的风险认知以及风险之间的因果联系强度。神经网络模型用在生态风险管理的情景分析中，可以帮助矿区生态风险管理者选择不同的方案以达到管理目标，为在煤矿区建立资源、环境和人口协调发展的生态环境管理机制奠定了基础，从而为生态风险管理提供了一个新视角。 Abstract:There are the natures of instability and uncertainty in ecological risk management. Each stakeholder's cognition plays a crucial role in it. For this reason, the coal-mining environment managers need to choose appropriate measures to take stakeholders' interests in account in ecosystem management. But the crucial problems to be solved are how to get and adjust different stakeholders' perspectives on ecological risk and environmental management, and how to quantify their perspectives, and coordinate their conflicts. This research paper focused on coal-mining ecosystem, explored the application of fuzzy cognitive map (FCM) on getting stakeholders' perspectives and graph theory indices on quantifying them, and simulated the different ecological risk management policy options using FCM-based artificial neural network (ANN). The Fuzzy Cognitive Mapping was applied to develop a participatory environmental management model for the typical coal-mining area in Yangquan. The ANN was used to conduct simulations combining the initial state of variables and the adjacency matrix getting from the social condensed map, The results showed that different stakeholders' cognition were distinctly different, managers emphasized national policy and economic interests brought by coal-mining activity, while engineers stressed living standard more, whereas mine workers and local residents paid more attention to the work's safety and their health condition. But it is noteworthy that‘air pollution’and ‘water damage’were the variables which were mentioned by almost all of them. It revealed the severity of ‘air pollution’and‘water damage’. The most central variables in the social condensed map were‘national policy’and‘health’, as well as in the four stakeholder groups. The simulations of policy option reflected that when local governments and enterprises make management decisions in coal-mining ecosystem, they should place an emphasis on protecting farmland, increasing vegetation coverage, improving energy efficiency and recycling waste so as to decrease air pollution, protect water environment and reduce resources waste. It is helpful for learning how different choices affect the coal-mining ecosystem. The results revealed that FCM analysis and ANN simulations could provide useful information for the development of environmental management planning. FCM analysis could help us to understand the varying attitudes among stakeholder groups as they considered environmental issues affecting the ecosystem. In addition, the policy option simulations based on ANN can help the coal-mining environmental managers to choose proper measures to achieve management goals, so as to lay the foundation of ecological environment management mechanism that can harmonize the development of resource, environment and population. Throughout the full text, the FCM method has the advantages: (1) Comparing with the traditional questionnaires, semi-structured interview respects more individual's perception and leaves more space to interviewees. (2) Comparing with the traditional evaluating factor system, the FCM method can get a more accurate causal relationship among each factor. (3) Getting knowledge easily, combining qualitative analysis and quantitative calculation, and expressing cognition and inference well. (4) Getting multi-stakeholders' FCMs and comparing their different perceptions is more convenient. Moreover, this paper expanded the environment management research methods and provided some new ideas and approaches with regional ecological risk management theoretically. 参考文献 相似文献 引证文献

The research paper presents the result of a study on the integration of two organizations from the same corporation, both players in the IT and Communications field. The complex business transformation environment is analyzed using the Fuzzy Cognitive Maps method in order to enhance the organizational system model obtained in a previous research through Soft Systems Methodology. The perception of 23 stakeholders is modelled and their individual cognitive maps about the transformation are drawn. These maps are transformed into individual adjacency matrices which are later augmented to form a social cognitive map containing 78 variables specific to the researched situation. The structure of the social cognitive map is analysed using graph theory and different evolution scenarios are simulated through neuronal network computation. The researchers will benefit from following the research method while the practioners will be offered generalised conclusions which can be considered in managing similar situations.

Fuzzy cognitive maps (FCM) are fuzzy signed directed graphs with feedbacks; they are simple and powerful tool for simulation and analysis of complex, nonlinear dynamic systems. However, FCM models are created by human experts mostly, and so built FCM models are subjective and building a FCM model becomes harder as number of variables increases. So in the last decade several methods are proposed providing automated generation of fuzzy cognitive maps from data. The main drawback of the proposed automated methods is their weaknesses on handling with large number of variables. The proposed method brings out a new strategy called concept by concepts approach (CbC) approach for learning of FCM. It enables the generation of large sized FCM models with a high precision and in a rapid way using the historical data.

This paper examines the use of fuzzy cognitive maps (FCMs) as a technique for modeling political and strategic issues situations and supporting the decision-making process in view of an imminent crisis. Its object domain is soft computing using as its basic elements different methods from the areas of fuzzy logic, cognitive maps, neural networks and genetic algorithms. FCMs, more specifically, use notions borrowed from artificial intelligence and combine characteristics of both fuzzy logic and neural networks, in the form of dynamic models that describe a given political setting. The present work proposes the use of the genetically evolved certainty neuron fuzzy cognitive map (GECNFCM) as an extension of certainty neuron fuzzy cognitive maps (CNFCMs) aiming at overcoming the main weaknesses of the latter, namely the recalculation of the weights corresponding to each concept every time a new strategy is adopted. This novel technique combines CNFCMs with genetic algorithms (GAs), the advantage of which lies with their ability to offer the optimal solution without a problem-solving strategy, once the requirements are defined. Using a multiple scenario analysis we demonstrate the value of such a hybrid technique in the context of a model that reflects the political and strategic complexity of the Cyprus issue, as well as the uncertainties involved in it. The issue has been treated on a purely technical level, with distances carefully kept concerning all sides involved in it.

This paper presents Fuzzy Cognitive Maps as an approach in modeling the behavior and operation of complex systems. This technique is the fusion of the advances of the fuzzy logic and cognitive maps theories, they are fuzzy weighted directed graphs with feedback that create models that emulate the behavior of complex decision processes using fuzzy causal relations. There are some applications in diverse domains (manage, multiagent systems, etc.) and novel works (dynamical characteristics, learning procedures, etc.) to improve the performance of these systems. First the description and the methodology that this theory suggests is examined, also some ideas for using this approach in the control process area, and then the implementation of a tool based on Fuzzy Cognitive Maps is described. The application of this theory in the field of control and systems might contribute to the progress of more intelligent and independent control systems. Fuzzy Cognitive Maps have been fruitfully used in decision making and simulation of complex situation and analysis.

Fuzzy cognitive maps (FCM) is a method to update a given initial vector to obtain the most stable state of a system, using a neighborhood of weights between these vectors and updating it over a series of iterations. FCMs are modeled with graphs. Neighbor weights between nodes are between -1 and 1. Nowadays it is used in business management, information technology, communication, health and medical decision making, engineering and computer vision. In this study, a dynamic FCM structure based on Particle Swarm Optimization (PSO) is given for determining node weights and online updating for modeling of dynamic systems with FCMs. Neighborhood weights in dynamic FCMs can be updated instantly and the system feedback is used for this update. In this work, updating the weights of the dynamic FCM is a PSO based approach that takes advantage of system feedback. In previous literature suggestions, dynamic FCM structure performs the weight updating process by using rule-based methods such as Hebbian. Metaheuristic methods are less complex and more efficient than rule-based methods in such optimization problems. In the developed PSO approach, the initialize vector state of the system, the weights between the vector nodes, and the desired steady state vector are taken into consideration. As a fitness function, the system has benefited from the convergence state to the desired steady state vector. As a stopping criterion for PSO, 100 * n number of iteration limits have been applied for the initial vector with n nodes. The proposed method has been tested for five different scenarios with different node counts.