基于最大矩阵元法确定聚类数的软测量建模方法

采用模糊C均值聚类算法(FCM)与支持向量机(SVM)相结合的多模型建模方法:较单一支持向量机软测量模型而言,可以有效解决复杂工业对象的强非线性和大工况范围的问题。但是传统的模糊C均值聚类算法必须依赖先验知识预先确定聚类个数。本文通过建立样本间的相似矩阵,利用模糊聚类最大矩阵元法确定FCM最佳聚类个数,再由FCM对训练样本数据进行聚类并用SVM构建组合软测量模型,得到多模型软测量系统。在对双酚A结晶单元工艺分析的基础上,将该方法:应用于结晶单元苯酚含量的软测量建模,仿真结果:证明该建模方法:提高了模型的估计精度,具有更好的可行性和有效性,能够满足工业生产的要求。     

模糊认知图研究进展

模糊认知图(Fuzzy Cognitive Map,FCM)作为知识表示、推理和软计算方法,通过在传统认知图模型中引入模糊测度来量化概念(concept)间因果关系的影响程度,近年来已成为国内外的研究热点.从研究进展的视角,归纳了FCM的基本框架和推理机制,总结了主流研究中FCM的基本类型,分析了FCM学习算法的主要特征,提出了今后专题研究方向的基本设想,以期对后续研究有所助益.     

基于有序加权平均算子的概率模糊认知图

模糊认知图(FCM)与概率模糊认知图(PFCM)使用简单的加权和集结因果推理结果,忽略了原因节点间关联关系的不确定性,阈值函数导致推理结果进一步失真.在继承FCM与PFCM优点的基础上,引入有序加权平均(OWA)算子模拟各种确定的或模糊的与或组合关系,提出了基于有序加权平均算子的概率模糊认知图(OWA-PFCM).通过构建一个动态的攻击效能评估模型,阐述了OWA-PFCM在工程建模中的应用.OWA-PFCM能同时表示因果节点状态的不确定性、因果联系强度的不确定性、与或组合关系的不确定性,具有更强的模拟能力.     

模糊认知图研究进展

模糊认知图(Fuzzy Cognitive Map, FCM)作为知识表示、推理和软计算方法,通过在传统认知图模型中引入模糊测度来量化概念(concept)间因果关系的影响程度,近年来已成为国内外的研究热点。从研究进展的视角,归纳了FCM的基本框架和推理机制,总结了主流研究中FCM的基本类型,分析了FCM学习算法的主要特征,提出了今后专题研究方向的基本设想,以期对后续研究有所助益。     

基于划分自适应融合的多视角模糊聚类算法

针对多视角聚类任务如何更好地实现视角间的合作之挑战, 提出一种新的视角融合策略. 该策略首先为每个视角设置一个划分, 然后通过自适应学习获取一个融合权重矩阵对每个视角的划分进行自适应融合, 最终利用视角集成方法得到全局划分结果. 将上述策略应用到经典的FCM(Fuzzy ??-means) 模糊聚类框架, 提出相应的多视角模糊聚类算法. 在模拟数据集和UCI 数据集上的实验结果均显示, 所提出的算法较几种相关聚类算法在应对多视角聚类任务时具有更好的适应性和更好的聚类性能.

     

模糊判断矩阵环境下的大型群决策方法

为解决大型的群决策问题,对传统的模糊C均值算法(FCM)进行了扩展。通过扩展的算法对专家个体模糊判断矩阵聚类,获取模糊划分矩阵和聚类原型,根据模糊划分矩阵确定类权重,进而利用WAA算子对聚类原型进行集结,求取群综合模糊判断矩阵。通过算例验证了该算法的可行性。     

模糊粒子群神经网络算法及应用

针对流程工业神经网络建模时,BP算法的局部收敛问题,采用模糊粒子群算法改进神经网络学习问题。该算法将模糊粒子群引入神经网络学习算法,使得粒子群的权重自适应更新,同时模糊粒子群自适应调整神经网络权重参数,改进网络收敛性。将算法用于建立乙烯裂解炉出口温度(COT)、裂解产品收率软测量模型,取得了较好的应用效果。     

基于关联属性的系统结构分析模型及其应用

为了减少主观因素对系统结构分析的干扰,充分考虑系统中要素、属性的关联影响,提出一种基于关联属性的系统结构分析模型.该模型基于定性分析构建判断矩阵,并利用确知属性权重代替属性影响权重建立影响关系矩阵,从而通过区分属性关联与否构造关联要素属性对属性的模糊关系矩阵,得到正、逆和双关联属性的贡献度与关联度,实现定性问题的定量分析.应用实例表明,该模型具有较强的实用性和可行性.     

基于FCM的神经网络建模及其在智能驾驶中应用研究

首先对模糊c-均值聚类算法进行了分析,然后把改进的FCM算法和RBF神经网络结 合起来建模,得到一种映射能力较强的自组织RBF神经网络.最后把它应用到智能驾驶中对驾 驶员的熟练程度和疲劳程度进行识别,得到了满意的结果.     

基于模糊认知网络的改进非线性Hebbian算法

针对难以用机理模型准确描述的非线性系统,研究基于模糊认知网络(fuzzy cognitive networks,FCN)的非线性系统建模和参数辨识问题.首先,建立非线性系统的具有数值推理和模糊信息表达的模糊认知网络模型,利用包含节点、权值和反馈的有向图表示系统.其次,由于模型的精确性取决于权值参数,提出了一种带终端约束的非线性Hebbian学习算法(nonlinear Hebbian learning,NHL).该算法在权值的学习过程中引入了FCN模型中节点的系统实际值,在原更新机制的基础上,增加了包含反馈值与预测值差值的修正项,然后归一化得到最终权值迭代公式.该算法具有收敛速度快、学习结果精准等优点,解决了传统非线性Hebbian算法对初始值依赖性强的缺点.最后将所提出的方法运用到水箱控制系统,仿真结果说明了基于FCN的非线性Hebbian学习算法的有效性.     

Inferring causal networks using fuzzy cognitive maps and evolutionary algorithms with application to gene regulatory network reconstruction

Fuzzy cognitive maps have been widely used as abstract models for complex networks. Traditional ways to construct fuzzy cognitive maps rely on domain knowledge. In this paper, we propose to use fuzzy cognitive map learning algorithms to discover domain knowledge in the form of causal networks from data. More specifically, we propose to infer gene regulatory networks from gene expression data. Furthermore, a new efficient fuzzy cognitive map learning algorithm based on a decomposed genetic algorithm is developed to learn large scale networks. In the proposed algorithm, the simulation error is used as the objective function, while the model error is expected to be minimized. Experiments are performed to explore the feasibility of this approach. The high accuracy of the generated models and the approximate correlation between simulation errors and model errors suggest that it is possible to discover causal networks using fuzzy cognitive map learning. We also compared the proposed algorithm with ant colony optimization, differential evolution, and particle swarm optimization in a decomposed framework. Comparison results reveal the advantage of the decomposed genetic algorithm on datasets with small data volumes, large network scales, or the presence of noise.     

新颖的模糊认知图学习方法解决工业控制问题

介绍的是基于量子粒子群算法模糊认知图的学习方法。其主要的思路是更新模糊认知图中能够使之趋向所要求的稳定状态的非零权值。将所研究的方法运用到工业控制问题,具有很大的现实意义。实验的结果表明,该方法是有效的,并优于传统的粒子群算法。     

基于相似粗糙集和模糊认知图的文本分类研究

阐述了基于相似粗糙集和模糊认知图的文本分类问题,提出了一种基于模糊认知图的文本分类推理算法,使文本分类成为一个基于文本特征项的权和特征项与类别的相关度构成的模糊认知图进行推理的结果,最后对该算法进行了实验,并对结果进行了分析.     

An experimental study of the dynamics of the certainty neuron fuzzy cognitive maps

Certainty Neurons have been introduced as a new type of artificial neurons that use a two variable transfer function that provides them with memory capabilities and decay mechanism. They are used in fuzzy cognitive maps which is an artificial neural network structure that creates models as collections of concepts – neurons and the various causal relationships – weighted arcs that exist between them. An experimental study of the certainty neuron fuzzy cognitive maps (CNFCMs) dynamical behaviour is presented as this appears through simulations. Two control parameters are used: the symmetry of the system's weight matrix and the strength of the decay mechanism. The values of these two parameters can lead the system to exhibit stable fixed point behaviour, limit cycle behaviour or to collapse. The ways that the two control parameters cause the change of the system's dynamical behaviour from fixed point to limit cycle are also presented. The areas where the systems exhibit specific dynamical behaviour are identified.     

Parameter identification of PWARX models using fuzzy distance weighted least squares method

PieceWise AutoRegressive eXogenous (PWARX) models represent one of the broad classes of the hybrid dynamical systems (HDS). Among many classes of HDS, PWARX model used as an attractive modeling structure due to its equivalence to other classes. This paper presents a novel fuzzy distance weight matrix based parameter identification method for PWARX model. In the first phase of the proposed method estimation for the number of affine submodels present in the HDS is proposed using fuzzy clustering validation based algorithm. For the given set of input–output data points generated by predefined PWARX model fuzzy c-means (FCM) clustering procedure is used to classify the data set according to its affine submodels. The fuzzy distance weight matrix based weighted least squares (WLS) algorithm is proposed to identify the parameters for each PWARX submodel, which minimizes the effect of noise and classification error. In the final phase, fuzzy validity function based model selection method is applied to validate the identified PWARX model. The effectiveness of the proposed method is demonstrated using three benchmark examples. Simulation experiments show validation of the proposed method.     

Distributed Computation Multi-agent System

This article addresses a formal model of a distributed computation multi-agent system. This model has evolved from the experimental research on using multi-agent systems as a ground for developing fuzzy cognitive maps. The main paper contribution is a distributed computation multi-agent system definition and mathematical formalization based on automata theory. This mathematical formalization is tested by developing distributed computation multi-agent systems for fuzzy cognitive maps and artificial neural networks – two typical distributed computation systems. Fuzzy cognitive maps are distributed computation systems used for qualitative modeling and behavior simulation, while artificial neural networks are used for modeling and simulating complex systems by creating a non-linear statistical data model. An artificial neural network encapsulates in its structure data patterns that are hidden in the data used to create the network. Both of these systems are well suited for formal model testing. We have used evolutionary incremental development as an agent design method which has shown to be a good approach to develop multi-agent systems according to the formal model of a distributed computation multi-agent system.     

Fuzzy decision maps: a generalization of the DEMATEL methods

The Decision making trial and evaluation laboratory (DEMATEL) method is used to build and analyze a structural model with causal relationships between different criteria. In this paper, it shows that DEMATEL is the specific case of fuzzy decision maps (FDM) when the threshold function is linear. Both FDM and DEMATEL have the same direct and indirect influence matrix. FDM incorporates the eigenvalue method, the fuzzy cognitive maps, and the weighting equation. In addition two numerical examples are illustrated to demonstrate the proposed results. On the basis of the mathematical proof and numerical results, we can conclude that FDM is a generalization of DEMATEL method.     

A new approach to fuzzy modeling

This paper proposes a new approach to fuzzy modeling. The suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as Takagi and Sugeno's model (1985), because it has the same structure as that of Takagi and Sugeno's model. It is also as easy to implement as Sugeno and Yasukawa's model (1993) because its identification mimics the simple identification procedure of Sugeno and Yasukawa's model. The suggested algorithm is composed of two steps: coarse tuning and fine tuning. In coarse tuning, fuzzy C-regression model (FCRM) clustering is used, which is a modified version of fuzzy C-means (FCM). In fine tuning, gradient descent algorithm is used to precisely adjust parameters of the fuzzy model instead of nonlinear optimization methods used in other models. Finally, some examples are given to demonstrate the validity of this algorithm     

模糊权值网络最小生成树问题的矩阵算法

通过研究模糊权值网络中的最小生成树问题,使用基于模糊数的结构元加权序和经典最小生成树问题的改进权矩阵法,本文提出一种求解边权值为三角模糊数的模糊权值网络最小生成树问题的矩阵算法,并对算法的复杂度和正确性进行分析。通过实例验证了该算法的有效性。