A novel hybrid method based on fuzzy cognitive maps and fuzzy clustering algorithms for grading celiac disease

This paper presents a new method based on fuzzy cognitive map (FCM) and possibilistic fuzzy c-means (PFCM) clustering algorithm for categorizing celiac disease (CD). CD is a complex disorder whose development is affected by genetics (HLA alleles) and gluten ingestion. 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 are of paramount importance. The proposed FCM models human thinking for the purpose of classifying patients suffering from CD. We used the latest grading method where three grades A, B1, and B2 are used. To improve FCM efficiency and classification capability, a nonlinear Hebbian learning algorithm is applied for adjusting the FCM weights. To this end, 89 cases are studied. Three experts extracted seven main determinant characteristics of CD which were considered as FCM concepts. The mutual effects of these concepts on one another and on the final concept were expressed in the form of fuzzy rules and linguistic variables. Using the center of gravity defuzzifier, we obtained the numerical values of these weights and obtained the total weight matrix. Ultimately, combining the FCM model with PFCM algorithm, we obtained the grades A, B1, and B2 accuracies as 88, 90, and 91%, respectively. The main advantage of the proposed FCM is the good transparency and interpretability in the decision-making procedure, which make it a suitable tool for daily usage in the clinical practice.

     

基于模糊认知图的文本分类推理算法

文本分类是信息处理的重要研究方向,现在应用较多的是基于统计计算的分类方法。介绍了利用模糊认知图的文本分类推理理论与算法,该方法是基于数值推理的,实现将统计与规则融合推理,灵活性较大,不需要语料的多次训练,适合于训练不充分和新主题的文本分类和多类分类,并具有一定的鲁棒性。     

A new methodology for Decisions in Medical Informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques

In this research work, a novel framework for the construction of augmented Fuzzy Cognitive Maps based on Fuzzy Rule-Extraction methods for decisions in medical informatics is investigated. Specifically, the issue of designing augmented Fuzzy Cognitive Maps combining knowledge from experts and knowledge from data in the form of fuzzy rules generated from rule-based knowledge discovery methods is explored. Fuzzy cognitive maps are knowledge-based techniques which combine elements of fuzzy logic and neural networks and work as artificial cognitive networks. The knowledge extraction methods used in this study extract the available knowledge from data in the form of fuzzy rules and insert them into the FCM, contributing to the development of a dynamic decision support system. The fuzzy rules, which derived by these extraction algorithms (such as fuzzy decision trees, association rule-based methods and neuro-fuzzy methods) are implemented to restructure the FCM model, producing new weights into the FCM model, that initially structured by experts. Concluding, our scope is to present a new methodology through a framework for decision making tasks using the soft computing technique of FCMs based on knowledge extraction methods. A well known medical decision making problem pertaining to the problem of radiotherapy treatment planning selection is presented to illustrate the application of the proposed framework and its functioning.     

Interrogating the structure of fuzzy cognitive maps

 Causal algebra in fuzzy cognitive maps (FCMs) plays a critical role in the analysis and design of FCMs. Improving causal algebra in FCMs to model complicated situations has been one of the major research topics in this area. In this paper we propose a dynamic causal algebra in FCMs which can improve FCMs' inference and representation capability. The dynamic causal algebra shows that the indirect, strongest, weakest and total effects a vertex influences another in the FCM not only depend on the weights along all directed paths between the two vertices but also the states of the vertices on the directed paths. Therefore, these effects are nonlinear dynamic processes determined by initial conditions and propagated in the FCM to reach a static or cyclic pattern. We test our theory with a simple example.     

On causal inference in fuzzy cognitive maps

Fuzzy cognitive maps (FCM) is a powerful paradigm for representing human knowledge and causal inference. This paper formally analyzes the causal inference mechanism of FCM. We focus on binary concept states. It is known that given initial conditions, FCM is able to reach only certain states in its state space. We prove that the problem of finding whether a state is reachable in the FCM is nondeterministic polynomial (NP) hard, that we can divide fuzzy cognitive maps containing circles into basic FCM modules. The inference patterns in these basic modules can be studied individually in a hierarchical fashion. This paper also presents a recursive formula for computing FCM's inference patterns in terms of key vertices. The theoretical results presented in this paper provide a feasible and effective framework for the analysis and design of fuzzy cognitive maps in real-world large-scale applications     

Some models of qualitative dynamic cognitive maps with fuzzy states

Some models of dynamic cognitive maps, whose factors are determined in finite linearly ordered qualitative scales, are studied. Notions of fuzzy values and increments of factors and operations over them are determined. Specific features of defuzzification of fuzzy qualitative values are discussed. Basic behavior effects of these models, sources and forms of data fuzziness in the computing process, means for controlling this event, and confidence limits in the simulating process are studied.     

Learning of fuzzy cognitive maps using density estimate

Fuzzy cognitive maps (FCMs) are convenient and widely used architectures for modeling dynamic systems, which are characterized by a great deal of flexibility and adaptability. Several recent works in this area concern strategies for the development of FCMs. Although a few fully automated algorithms to learn these models from data have been introduced, the resulting FCMs are structurally considerably different than those developed by human experts. In particular, maps that were learned from data are much denser (with the density over 90% versus about 40% density of maps developed by humans). The sparseness of the maps is associated with their interpretability: the smaller the number of connections is, the higher is the transparency of the map. To this end, a novel learning approach, sparse real-coded genetic algorithms (SRCGAs), to learn FCMs is proposed. The method utilizes a density parameter to guide the learning toward a formation of maps of a certain predefined density. Comparative tests carried out for both synthetic and real-world data demonstrate that, given a suitable density estimate, the SRCGA method significantly outperforms other state-of-the-art learning methods. When the density estimate is unknown, the new method can be used in an automated fashion using a default value, and it is still able to produce models whose performance exceeds or is equal to the performance of the models generated by other methods.     

Ranking fuzzy cognitive map based scenarios with TOPSIS

Scenarios describe events and situations that would occurred in the future real-world. Policy makers use scenario methods as a tool to build landscapes of possible futures at a national level. Based on these future visions, policy and decision-makers are able to explore different courses of action. In recent years, the number of potential scenario methods and applications is increasing. It is because academics and practitioners are increasing their interest about it. In spite of the success of scenario methods’ support, scenario-based decision making still is not a fully structured process.The proposed methodology aims to bring methodological support to scenario-based decision making in scenario analysis. The originality of the proposed approach with respect to other ones is that it aims to use the scenarios’ assessment and ranking as a whole. Traditional approaches consider the future impact of each present entity in isolation. This assumption is a simplification of a more complex reality, in which different entities interact with each other. The model that the authors propose allows decision and policy makers to measure the impact of a entity interactions. To reach this aim, the proposal combine Delphi method, soft computing (fuzzy cognitive maps) and multicriteria (TOPSIS) techniques. In addition, a numerical example is developed for illustrating the proposal.     

On a semantics for neural networks based on fuzzy quantifiers

We describe the aggregation process of the typical artificial neuron. We introduce the concept of a fuzzy linguistic quantifier and describe the process for determining the truth of propositions containing linguistic quantifiers. We show how this truth value can be viewed as the firing level of an artificial neuron. We show the relationship between fuzzy sets and neural inputs. A new class of neurons called owa-neurons is described. A learning algorithm for this class of neurons is presented. We provide a methodology for processing information in non-numeric neural networks. © 1992 John Wiley & Sons, Inc.     

The challenge of modelling supervisory systems using fuzzy cognitive maps

This paper examines fuzzy cognitive map (FCM) theory and its use in supervisory control systems. An FCM is a graph used to depict cause and effect between concepts that stand for the states and variables of the system. An FCM represents the whole system in a symbolic manner, just as humans have stored the operation of the system in their brains, thus it is possible to help man's intention for more intelligent and autonomous systems. FCM representation, construction and a mathematical model are examined; a generic system is proposed and the implementation of FCM in a process control problem is illustrated and a model for supervisors of manufacturing systems is discussed. Although an FCM seems to be a simple model of system behaviour, it appears to be a powerful and effective tool describing the behaviour of a system and representing the accumulated knowledge of a system.     

Augmented fuzzy cognitive maps for modelling LMS critical success factors

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.     

Improving fuzzy cognitive maps learning through memetic particle swarm optimization

Fuzzy cognitive maps constitute a neuro-fuzzy modeling methodology that can simulate complex systems accurately. Although their configuration is defined by experts, learning schemes based on evolutionary and swarm intelligence algorithms have been employed for improving their efficiency and effectiveness. This paper comprises an extensive study of the recently proposed swarm intelligence memetic algorithm that combines particle swarm optimization with both deterministic and stochastic local search schemes, for fuzzy cognitive maps learning tasks. Also, a new technique for the adaptation of the memetic schemes, with respect to the available number of function evaluations per application of the local search, is proposed. The memetic learning schemes are applied on four real-life problems and compared with established learning methods based on the standard particle swarm optimization, differential evolution, and genetic algorithms, justifying their superiority.     

Computer networks design using hybrid fuzzy expert systems

 Designing and configuring large computer networks to support a variety of applications and computational environments is difficult, as it not only requires highly specialized technical skills and knowledge, but also a deep understanding of a dynamic commercial market. Hybrid fuzzy expert systems integrate fuzzy expert systems and neural networks methods replacing classical hard decision methods and providing better performance than traditional techniques. In this paper, we present an integrated fuzzy expert system, machine learning, and neural networks approach to large structured computer networks design and evaluation. After presenting an overview of the system and the major research choices, we describe in detail the system's modules and present examples of its potential use.     

Various hybrid methods based on genetic algorithm with fuzzy logic controller

In this paper we propose several efficient hybrid methods based on genetic algorithms and fuzzy logic. The proposed hybridization methods combine a rough search technique, a fuzzy logic controller, and a local search technique. The rough search technique is used to initialize the population of the genetic algorithm (GA), its strategy is to make large jumps in the search space in order to avoid being trapped in local optima. The fuzzy logic controller is applied to dynamically regulate the fine-tuning structure of the genetic algorithm parameters (crossover ratio and mutation ratio). The local search technique is applied to find a better solution in the convergence region after the GA loop or within the GA loop. Five algorithms including one plain GA and four hybrid GAs along with some conventional heuristics are applied to three complex optimization problems. The results are analyzed and the best hybrid algorithm is recommended.     

基于神经网络的自适应模糊控制系统

针对啤酒发酵过程中罐内温度控制问题,研究神经网络对模糊控制规则的优化方法,利用径向基函数神经网络对模糊控制规则进行优化,提高其自适应能力。以啤酒生产过程中主发酵阶段的数据作为输入样本,通过径向基函数神经网络进行学习训练,校正模糊控制规则,优化模糊控制器。优化前与优化后响应特性曲线的比较结果表明, RB F神经网络学习能力强,收敛速度快;模糊控制规则的完备性和一致性明显改善,控制器的响应速度快、超调量小、稳定性强、控制效果好。     

Self-organizing neurofuzzy networks based on evolutionary fuzzy granulation

Experimental software datasets describing software projects in terms of their complexity and development time have been a subject of intensive modeling. A number of various modeling methodologies and modeling designs have been proposed including such development frameworks as neural networks, fuzzy and neurofuzzy models. In this study, we introduce a concept of self-organizing neurofuzzy networks (SONFN), a hybrid modeling architecture combining neurofuzzy networks (NFN) and polynomial neural networks (PNN). For these networks we develop a comprehensive design methodology. The construction of SONFNs takes advantage of the well-established technologies of computational intelligence (CI), namely fuzzy sets, neural networks and genetic algorithms. The architecture of the SONFN results from a synergistic usage of NFNs and PNNs. NFN contributes to the formation of the premise part of the rule-based structure of the SONFN. The consequence part of the SONFN is designed using PNNs. We discuss two types of SONFN architectures whose taxonomy is based on the NFN scheme being applied to the premise part of SONFN. We introduce a comprehensive learning algorithm. It is shown that this network exhibits a dynamic structure as the number of its layers as well as the number of nodes in each layer of the SONFN are not predetermined (as this is the case in a popular topology of a multilayer perceptron). The experimental results include a well-known NASA dataset concerning software cost estimation.     

Image restoration techniques based on fuzzy neural networks

By establishing some suitable partitions of input and output spaces, a novel fuzzy neural network (FNN) which is called selection type FNN is developed. Such a system is a multilayer feedforward neural network, which can be a universal approximator with maximum norm. Based on a family of fuzzy inference rules that are of real senses, a simple and useful inference type FNN is constructed. As a result, the fusion of selection type FNN and inference type FNN results in a novel filter-FNN filter. It is simple in structure. And also it is convenient to design the learning algorithm for structural parameters. Further, FNN filter can efficiently suppress impulse noise superimposed on image and preserve fine image structure, simultaneously. Some examples are simulated to confirm the advantages of FNN filter over other filters, such as median filter and adaptive weighted fuzzy mean (AWFM) filter and so on, in suppression of noises and preservation of image structure.     

Learning and clustering of fuzzy cognitive maps for travel behaviour analysis

In modern society, more and more attention is given to the increase in public transportation or bike use. In this regard, one of the most important issues is to find and analyse the factors influencing car dependency and the attitudes of people in terms of preferred transport mode. Although the individuals’ transport behavioural modelling is a complex task, it has a notable social and economic impact. Thus, in this paper, fuzzy cognitive maps are explored to represent the behaviour and operation of such complex systems. This soft-computing technique allows modelling how the travellers make decisions based on their knowledge of different transport modes properties at different levels of abstraction. These levels correspond to the hierarchy perception including different scenarios of travelling, different benefits of choosing a specific travel mode, and different situations and attributes related to those benefits. We use learning and clustering of fuzzy cognitive maps to describe travellers’ behaviour and change trends in different abstraction levels. Cluster estimations are done before and after the learning of the maps, in order to compare people’s way of thinking if only considering an initial view of a transport mode decision for a daily activity, and when they really have a deeper reasoning process in view of benefits and consequences. The results of this study will help transportation policy decision makers in better understanding of people’s needs and consequently will help them actualizing different policy formulations and implementations.