基于自组织映射和模糊隶属度的财务危机预警研究

良好的财务危机预警模型能够有效监控企业运营情况,避免企业倒闭或被重组的悲剧发生。本文结合自组织映射模型和模糊C均值的模糊隶属度,构造模糊自组织映射模型,并应用到财务危机预警中。该模型将模糊隶属度带入到学习率函数中,在计算过程中自动更新获胜节点邻域范围,并在迭代过程中采用批学习算法,提高预测精度、稳定输出结果。对沪深两市上市公司的财务指标进行实证研究,通过与传统预警模型对比,得出模糊自组织映射模型在财务危机预警方面具有更优越的预测性能。     

基于旋转算法的随机模糊均值-方差投资组合优化

Markowitz首先采用方差度量风险，并应用于投资组合优化中，大多数的均值方差模型仅对随机投资组合优化或模糊投资组合优化进行研究，然而，实际投资组合优化问题既包含随机信息也包含模糊信息。本文首先定义随机模糊变量的方差，并用其度量风险，提出了具有交易成本、借贷约束和阀值约束的均值-方差随机模糊投资组合优化模型。基于随机模糊理论，将上述模型转化为具有线性等式和线性不等式约束的凸二次规划问题，并得到其KKT条件。本文还提出改进的旋转算法求解上述模型，该算法消掉KKT条件中部分变量，减少计算量。最后，采用中国证券市场的实际数据进行样本内分析和样本外分析，验证了上述模型和算法的有效性。     

基于组合赋权的难采储量模糊分类

选取两个效果指标,结合模糊C均值算法和组合赋权法实现难采储量的分类.首先基于效果指标运用模糊C均值算法自动搜索储量的最佳类别,再利用主客观赋权偏差最小的思想,构建组合赋权模型,确定属性指标的权重,并计算储量效益指标值,结合模糊C均值结果判别难采储量类别.最后以大庆某油田为实例,对其难采储量进行了分类,有效指导难采储量滚动开发决策.     

基于混合单纯形算法的模糊均值图像分割

针对模糊C均值算法用于图像分割时对初始值敏感、容易陷入局部极值的问题,提出基于混合单纯形算法的模糊均值图像分割算法.算法利用Nelder-Mead单纯形算法计算量小、搜索速度快和粒子群算法自适应能力强、具有较好的全局搜索能力的特点,将混合单纯形算法的结果作为模糊C均值算法的输入,并将其用于图像分割.实验结果表明:基于混合单纯形算法的模糊均值图像分割算法在改善图像分割质量的同时,提高了算法的运行速度.     

可信性均值-绝对偏差投资组合优化

为了度量金融市场的不确定性,本文引入了模糊变量。假设资产收益率为模糊数,分别运用可信性均值和可信性绝对偏差度量投资组合的收益与风险。考虑到投资者偏好,分别提出了以收益最大化的均值-绝对偏差优化模型和以风险最小化的优化模型。基于可信性理论,将上述模型转化为线性规划问题,并运用旋转算法求解。通过实证研究,证明了该算法的有效性,并比较了两个模型在实际投资决策过程中的效率。结果表明,以收益最大化的均值-绝对偏差优化模型效率优于风险最小的优化模型。     

基于PSO-AFSA混合算法的模糊投资组合问题的研究

针对模糊不确定的证券市场,用可能性均值、下可能性方差和协方差分别替换了投资组合模型中概率均值、方差和协方差,构建了双目标均值-方差投资组合模型。然后采用线性加权法将双目标模型转化为单目标模型,进而提出了一个PSO-AFSA混合算法对其求解。该混合算法中,将粒子群算法搜索的结果作为人工鱼群算法初始鱼群,进一步搜索,这样能有效的避免粒子群算法陷入局部最优。同时,将人工鱼群中的最好位置反馈到粒子群算法的速度更新公式中,指引粒子运动,加快算法收敛。最后,进行实例分析,结果表明:PSO-AFSA混合算法是有效的,混合算法搜索到的全局最优值好于基本粒子群算法搜索到的全局最优值。     

基于FCM-组合赋权的难采储量分类

目前储量的分类标准是通过划分指标值的范围来确定的,这就要求所有指标值恰好符合既定的指标范围,否则难以划分储量类别。为克服这一问题,本文结合模糊c-均值算法和组合赋权法实现难采储量的分类。首先基于效益指标运用模糊c-均值算法自动搜索储量的最佳类别,再利用主客观赋权偏差最小的思想,构建组合赋权模型,确定属性指标的权重,并计算储量效益指标值,结合模糊c-均值结果判别难采储量类别。最后以大庆某油田为实例,对其难采储量进行了分类,有效指导难采储量滚动开发决策。     

最佳费用流

建立赋模糊数为费用权的容量－－费用网络中，据模糊决策来求解最佳费用流的网络模型，并给出这一模型的相应算法。     

一种基于CS-FCM算法的模糊时间序列预测模型

为了发挥模糊理论在不确定性预测中的优势并保留模糊时间序列(FTS)预测模型的可解释性,本文针对目前应用广泛的模糊C均值聚类(FCM)算法进行改进,提出了一种基于布谷鸟搜索的FCM (CS-FCM)算法.将CS-FCM算法用于模糊时间序列模型的非均匀论域划分与数据的模糊化处理,建立一种基于CS-FCM算法的模糊时间序列预测模型.该算法可实现聚类中心的全局寻优,降低传统FCM算法易陷入局部极小值带来的误差,提高模型预测精度.实证分析结果表明, CS-FCM算法的适应度优于FCM算法,本文模型的预测误差小于经典模糊时间序列预测模型,验证了新预测模型的有效性.     

基于模糊遗传算法的自融资有效投资组合研究

在马克维茨投资组合的均值一方差模型框架下,给出限制投资数量的自融资投资组合优化模型.把预期收益率不等式约束转化为模糊约束,采用一种通过惩罚因子,对适应度函数进行修正的模糊遗传算法来求解模型.在理论上,这种算法能够将最优基因较完整地遗传到下一代,有效地避免了早熟现象,可以得到更好的适应度函数值.在实际应用中,对一具体自融资有效投资组合实例进行计算,结果表明:本文所提出的模糊遗传算法是可行的、有效的,具有更好的优化结果.     

自组织结构的变论域模糊控制

针对变论域模糊控制,提出一种新的自组织结构的变论域模糊控制方法。自组织结构算法可以调整变论域模糊系统结构以及动态获得模糊规则,进一步减小变论域模糊控制项的稳态逼近误差。通过进一步理论分析可知,自组织结构算法仅仅保证了系统瞬时的切换是平稳的,但不能保证系统的闭环稳定性。给出了所提出控制方法的适用条件。通过与固定模糊系统结构的变论域模糊控制比较,仿真结果表明,所提出控制方法不仅使得系统的稳态跟踪误差更平稳,而且使得输入控制信号更加平滑。     

Fuzzy logic for auditory evoked response monitoring and control of depth of anaesthesia

This paper describes a self-organizing fuzzy model of patients undergoing surgery which was created from 10 clinical trials with off-line analysis during maintenance of anaesthesia using the drug propofol. The effects of patient sensitivity and surgical disturbances are also represented in this patient model. Hence, this model can be considered to be a qualitative pharmacologically related model for propofol during the anaesthetic maintenance stage. Furthermore, a closed-loop simulation has been designed to validate the patient model and compare the performance of a self-organizing fuzzy logic controller algorithm against a clinically derived linguistic controller. The successful results obtained provide proof-of-concept and encouragement to perform on-line clinical trials using fuzzy logic-based monitoring and control in operating theatre in the near future.     

A Nonlinear Adaptive Controller: A Comparison between Fuzzy Logic Control and Neurocontrol

This paper explores, from a surface-fitting viewpoint, two algorithmswhich are often applied in the field intelligent control: fuzzyself-organizing controllers and neural networks. Both methodologiesadapt internal model parameters in response to the plant's input-outputmapping. However, while the convergence of single-layer neuralnetworks has been studied in great detail, very few theoremshave been proved about self-organizing fuzzy logic controllers.In this paper, it is shown that B-splines can provide a frameworkfor choosing the shape of the fuzzy sets. Then the operatorschosen to implement the underlying fuzzy logic are examined,showing how they can produce ‘smooth’ control surfaces.It is now possible to make a direct comparison between fuzzylogic controllers and feedforward neural networks, demonstratingthat, in a forward-chaining mode, storing the plant's behaviourin terms of weights or rule confidences is equivalent. Finally,three training rules for the self-organizing fuzzy controllerare derived.     

Self-organizing adaptive wavelet CMAC backstepping control system design for nonlinear chaotic systems

A novel self-organizing wavelet cerebellar model articulation controller (CMAC) is proposed. This self-organizing wavelet CMAC (SOWC) can be viewed as a generalization of a self-organizing neural network and of a conventional CMAC, and it has better generalizing, faster learning and faster recall than a self-organizing neural network and a conventional CMAC. The proposed SOWC has the advantages of structure learning and parameter learning simultaneously. The structure learning possesses the ability of on-line generation and elimination of layers to achieve optimal wavelet CMAC structure, and the parameter learning can adjust the interconnection weights of wavelet CMAC to achieve favorable approximation performance. Then a SOWC backstepping (SOWCB) control system is proposed for the nonlinear chaotic systems. This SOWCB control system is composed of a SOWC and a fuzzy compensator. The SOWC is used to mimic an ideal backstepping controller and the fuzzy compensator is designed to dispel the residual of approximation errors between the ideal backstepping controller and the SOWC. Moreover, the parameters of the SAWCB control system are on-line tuned by the derived adaptive laws in the Lyapunov sense, so that the stability of the feedback control system can be guaranteed. Finally, two application examples, a Duffing–Holmes chaotic system and a gyro chaotic system, are used to demonstrate the effectiveness of the proposed control method. The simulation results show that the proposed SAWCB control system can achieve favorable control performance and has better tracking performance than a fuzzy neural network control system and a conventional adaptive CMAC.     

模糊软映射及其应用

在模糊软集合的基础上,提出了模糊软关系和模糊软映射的定义,并研究了它们的性质,最后给出了模糊软映射在医疗诊断中的应用实例.     

硬聚类和模糊聚类的结合——双层FCM快速算法

模糊c均值(FCM)聚类算法在模式识别领域中得到了广泛的应用,但FCM算法在大数据集的情况下需要大量的CPU时间,令用户感到十分不便,提高算法的速度是一个急待解决的问题。本文提出的双层FCM聚类算法是一种快速算法,它体现了硬聚类和模糊聚类的结合,以硬聚类的结果对模糊聚类的初始值进行指导,从而明显地缩短了迭代过程。双层FCM算法所用的CPU时间仅为FCM算法的十三分之一,因而具有很强的实用价值。     

A rule-based development of incremental models

In the study, we propose a concept of incremental fuzzy models in which fuzzy rules are aimed at compensating discrepancies resulting because of the use of a certain global yet simple model of general nature (such as e.g., a constant or linear regression). The structure of input data and error discovered through fuzzy clustering is captured in the form of a collection of fuzzy clusters, which helps eliminate (compensate) error produced by the global model. We discuss a detailed architecture of the proposed rule-based model and present its design based on an augmented version of Fuzzy C-Means (FCM). An extended suite of experimental studies offering some comparative analysis is covered as well.     

Fuzzy clustering algorithms for mixed feature variables

This paper presents fuzzy clustering algorithms for mixed features of symbolic and fuzzy data. El-Sonbaty and Ismail proposed fuzzy c-means (FCM) clustering for symbolic data and Hathaway et al. proposed FCM for fuzzy data. In this paper we give a modified dissimilarity measure for symbolic and fuzzy data and then give FCM clustering algorithms for these mixed data types. Numerical examples and comparisons are also given. Numerical examples illustrate that the modified dissimilarity gives better results. Finally, the proposed clustering algorithm is applied to real data with mixed feature variables of symbolic and fuzzy data.     

基于Fuzzy理论的一种医疗诊断模型

首先从大量文献中整理出不同经脉中穴位主治病症的个数及穴位在文献中出现的频数等数据,并对现有的模糊聚类分析方法进行了研究,依据改进后的FCM法对手少阳三焦经上的全部穴位按重要程度进行了分类.同时通过病症现象与穴位之间的隶属关系,提出由历史数据及专家优序数综合确定模糊隶属度的方法,建立模糊医疗诊断模型.该模型为利用经络中的穴位快速、准确治疗疾病提供了理论方法.