参数微扰激光超混沌系统模糊模型

研究了存在参数微扰的激光超混沌系统的Takagi-Sugeno (T-S) 模糊模型的逼近性及建立问题。分析了存在参数微扰的激光超混沌系统的动力学特性。基于典型T-S模糊系统的建模原理,研究了激光超混沌T-S模糊系统的通用逼近性,通过确定非线性关键变量、模糊隶属度函数及模糊规则,建立了激光超混沌系统的T-S模糊模型。仿真结果表明,所建立的模糊模型能够有效逼近存在参数微扰的激光超混沌系统。     

基于模糊树模型的混沌时间序列预测

应用模糊树模型,对混沌时间序列进行建模和预测.该方法可以根据建模数据在空间中的分布信息,基于二叉树结构自适应划分输入空间,得到模糊子空间,在与叶节点对应的子空间上建立线性函数作为模糊规则的后件,用隶属度函数将各分片线性函数光滑连接,最后得到一个精度比较高的非线性映射.通过对Mackey-Glass、Lorenz和Henon混沌时间序列的建模和预测研究,仿真结果表明,该方法具有建模精度高、运行速度快、泛化能力强、预测步数多、适用范围广等优点. 关键词： 模糊树模型 混沌时间序列 预测     

基于空间信息的直觉模糊C-均值图像分割算法

针对模糊C-均值聚类算法的单一隶属度不能充分描述图像不确定性,且聚类过程中忽略像素空间关系的问题,提出一种基于空间信息的直觉模糊C-均值算法;该算法选取3×3的模板计算邻域像素灰度均值;并引入权重项,来控制灰度信息和空间信息各自所占的比重,同时用犹豫度更新直觉模糊集的隶属度函数;对常用标准图像的仿真结果表明,该算法能更好地保留图像细节信息,得到更加理想的图像分割效果。     

基于最大隶属度原则的彩色图像分割方法

提出一种基于模糊最大隶属度原则的彩色图像分割方法.从彩色图像的彩色直方图中获取目标色和背景色，建立一组色彩模糊集，计算该图像中的色彩在各模糊集中的隶属度，并基于最大隶属度原则确定色彩的归属.基于上述最大隶属度的模糊模式识别原则建立了最大隶属度-径向基函数神经网络，实现了快速有效的彩色图像分割. 关键词： 最大隶属度原则 彩色图像分割 彩色直方图 神经网络     

基于模糊集的自适应红外图像边缘锐化算法

针对红外图像边缘模糊和非均匀性噪音强的特点,提出了一种基于模糊集的自适应红外图像边缘锐化方法.针对图像边缘细节和噪音难以表示和区分的特点,分别建立噪音、弱边缘和强边缘的模糊特征隶属度函数,并且提取图像信息自适应调整隶属度函数;通过隶属度函数将图像映射到模糊特征平面,由模糊特征平面控制图像边缘锐化系数.该方法不仅能够锐化红外图像边缘,而且改善了传统边缘锐化算法对图像噪音放大的缺点,避免了对强边缘的过渡增强导致图像出现过增强现象,改善了图像质量.     

基于最大隶属原则的核磁共振波谱模糊识别

介绍了基于最大隶属原则的核磁共振(NMR)波谱模糊识别原理与方法. 提出了关于核磁共振波谱模糊集合的概念,并给出了相应的隶属函数. 通过建立标准谱数据库和相应的模糊识别算法,实现了核磁共振波谱的快速自动定性分析. 对苯酚、邻苯二酚、间苯二酚及对苯二酚4种化合物及其11种混合物样品的¹H NMR谱进行了定性分析. 结果表明,在粗略找峰和隶属度阈值在0.45~0.85之间较大范围内取值的情况下,方法均给出了准确的识别结果.     

基于模糊神经网络的话者辩认研究

本文首先根据衡量话者特征参数有效性的F比值公式分析比较了LSP和CEP参数在描述话者特征方面的不同特性，确定LSP参数作为话者特征参数．接着提出了描述话者特征的层次模型．根据函数扩展网络的内插能力和隶属函数的模糊统计建立方法，用函数扩展网络实现模糊状态的隶属函数，由网络隶属函数构成话者特征层次模型的基本单元．以最大隶属度原则作为系统的辨认决策准则．在小词表（0－9十个数字）内，采用文本无关的方式进行42人的话者辨认实验，当测试语音由5个数字随机组合时系统正确辨认率为99．76％．     

核材料γ能谱指纹模糊识别机理研究

以γ射线的能量与强度所构成的二维平面为论域,提出了关于γ射线能谱的二维模糊集合的概念,建立了γ射线能谱二维模糊集合隶属函数.提出和介绍了γ射线能谱指纹模糊识别的基本原理与方法.通过对γ能谱数据的归一化处理,实现了γ射线能谱指纹的类型识别和个体识别.通过引入识别增强因子,获得了较高的识别置信度.对模拟放射性物质的γ射线能谱指纹进行了识别研究,探讨了系统统计涨落以及探测距离变化对识别隶属度的影响.研究表明,γ射线能谱指纹模糊识别方法具有较强的类型识别能力和个体识别能力,可应用于核安全保障等领域. 关键词： γ能谱指纹 模糊识别 核材料     

肿瘤周边组织拉曼光谱的模糊模式识别研究

基于模糊模式识别的基本理论,阐述了肿瘤周边组织拉曼光谱的预处理、特征提取和选择,根据这些特征改进了梯形分布的偏大型隶属函数,在40例样本的基础上建立了肿瘤周边组织拉曼光谱对恶性肿瘤的隶属函数,并根据此函数进行了分类器的训练学习,经过另40例样本的测试,恶性肿瘤的识别率为82.4%,非恶性肿瘤的识别率为73.9%,识别效果较为理想.     

一种基于混合人工鱼群的图像模糊增强算法

模糊隶属度函数的形式直接影响灰度图像增强的质量。为进一步改善图像模糊增强的效果,对目前的模糊隶属度函数进行研究,并提出一种改进的参数化 型模糊隶属度函数用于图像增强。所提算法利用图像对比度的质量评价模型,结合人工鱼群算法和Powell算法搜索 型函数中的未知参数值,进而确定该模糊隶属度函数。通过实验结果表明：该算法能够较好的改善灰度图像质量,并且控制参数可通过优化算法自适应获得,具有较好的通用性,是一种有效的图像模糊增强算法。     

Determination of Water Force Constants with Fuzzy Methods

The paper proposes a new approach to the determination of moleculare force fields from observed vibrational frequencies for the case, that observed frequencies are only given in more or less sharp expectation ranges (intervals). Starting from a physically reasonable force constant space, we determine force fields so that the calculated eigenfrequencies are near by the experimental ones. However, in contrast to classical least squaress methods, we will model the uncertainty expressed by the statement “near by” with special triangle functions where the experimental values stand in the head of the triangles. In the sense of the fuzzy set theory, these functions are interpreted as membership functions. Within the force constant space we carry out Normal Coordinate Analyses in the usual way and determine a force field so that the eigenfrequencies have the least possible deviation from the experimental values. The deviation is evaluated with the help of the triangle membership functions. Hence, the selected force field with least deviation is called a fuzzy-optimal one. The water molecule is selected as a model case, because its least-squares force field is precisely known. Following the Born-Oppenheimer-Approximation, we treat all water isotopes in the same manner and select fuzzy-optimal force fields so that the evaluations for each water isotope are approximately similar and as high as possible. We will see that the fuzzy approach is always applicable, even if the assumptions for application of least squares methods are failed. This is a main advantage of the fuzzy method.

     

垂直上升管中油水两相流流型辨识

基于运动波理论本文建立了垂直上升管中油水两相流运动波传播方程,计算得到了管内无仪器插入体情况下油水两相流运动波传播速度特性曲线,根据曲线特征确定了油水两相流过渡流型存在于持水率大于0.25并小于0.5区间,应用该流型辨识准则对Govier等实验观察到的油水两相流流型试验点进行了辨识,取得了较好对比效果。此外还使用Flores等击碎聚合机理性模型验证了本文的辨识流型图,并与之结合产生了更加细化的合成流型图。     

Mathematical modeling of randomness and fuzziness phenomena in scientific studies. II. Applications

This paper considers some elements of the optimal fuzzy decision theory that are similar to the optimal statistical decision theory, in particular, the theory of optimal fuzzy identification and optimal fuzzy hypothesis testing, such as Neyman–Pearson statistical hypothesis testing and optimal fuzzy estimation along with a sequential fuzzy identification algorithm similar to the Wald sequential statistical criterion. Some elements of the fuzzy measuring and computing transducer theory and its applications in the problems of the analysis and interpretation of measurement experiment data are given.     

Hybrid uncertainty propagation of coupled structural–acoustic system with large fuzzy and interval parameters

Based on the finite element framework and uncertainty analysis theory, this paper proposes a first-order subinterval perturbation finite element method (FSPFEM) and a modified subinterval perturbation finite element method (MSPFEM) to solve the uncertain structural–acoustic problem with large fuzzy and interval parameters. Fuzzy variables are used to represent the subjective uncertainties associated with the expert opinions; whereas, interval variables are adopted to quantify the objective uncertainties with limited information. By using the level-cut strategy and subinterval methodology, the original large fuzzy and interval parameters are decomposed into several subintervals with small uncertainty level. In both FSPFEM and MSPFEM, the subinterval matrix and vector are expanded by the Taylor series. The inversion of subinterval matrix in FSPFEM is approximated by the first-order Neumann series, while the modified Neumann series with higher order terms is adopted in MSPFEM. The eventual fuzzy interval frequency responses are reconstructed by the interval union operation and fuzzy decomposition theorem. A numerical example evidences the remarkable accuracy and effectiveness of the proposed methods to solve engineering structural–acoustic problems with hybrid uncertain parameters.     

Wave Simulation in Frozen Porous Media

We propose a numerical algorithm for simulation of wave propagation in frozen porous media, where the pore space is filled with ice and water. The model, based on a Biot-type three-phase theory, predicts three compressional waves and two shear waves and models the attenuation level observed in rocks. Attenuation is modeled with exponential relaxation functions which allow a differential formulation based on memory variables. The wavefield is obtained using a grid method based on the Fourier differential operator and a Runge–Kutta time-integration algorithm. Since the presence of slow quasistatic modes makes the differential equations stiff, a time-splitting integration algorithm is used to solve the stiff part analytically. The modeling is second-order accurate in the time discretization and has spectral accuracy in the calculation of the spatial derivatives.     

A simplified fuzzy model to mimic a nonlinear system, applied to aplasma source

This paper develops a fuzzy model to simulate the behaviour of a nonlinear system, in particular a plasma source, with a view to developing a control system for processing plasmas employing a helicon source. Genetic algorithms are employed to optimize fuzzy rules related to the parameters of the fuzzy model which contain a set of variable zeros and poles of the nonlinear system as well as its time delay. A practical application of the fuzzy model is to estimate the electron number density of a low-temperature plasma. Based on the membership functions of the input and output, a set of fuzzy rules by which the variable zeros and poles are identified is derived and optimized using a genetic algorithm. The principal reason for investigating the proposed fuzzy model is the subsequent computer-aided design of a fuzzy controller to control the nonlinear system. Two experimental results are presented to validate the fuzzy model method. One shows a computer simulation and the other predicts the real-time behaviour of the plasma source as its input parameters are varied     

A Decision Support Model for Hotel Recommendation Based on the Online Consumer Reviews Using Logarithmic Spherical Hesitant Fuzzy Information

Spherical hesitant fuzzy sets have recently become more popular in various fields. It was proposed as a generalization of picture hesitant fuzzy sets and Pythagorean hesitant fuzzy sets in order to deal with uncertainty and fuzziness information. Technique of Aggregation is one of the beneficial tools to aggregate the information. It has many crucial application areas such as decision-making, data mining, medical diagnosis, and pattern recognition. Keeping in view the importance of logarithmic function and aggregation operators, we proposed a novel algorithm to tackle the multi-attribute decision-making (MADM) problems. First, novel logarithmic operational laws are developed based on the logarithmic, t-norm, and t-conorm functions. Using these operational laws, we developed a list of logarithmic spherical hesitant fuzzy weighted averaging/geometric aggregation operators to aggregate the spherical hesitant fuzzy information. Furthermore, we developed the spherical hesitant fuzzy entropy to determine the unknown attribute weight information. Finally, the design principles for the spherical hesitant fuzzy decision-making have been developed, and a practical case study of hotel recommendation based on the online consumer reviews has been taken to illustrate the validity and superiority of presented approach. Besides this, a validity test is conducted to reveal the advantages and effectiveness of developed approach. Results indicate that the proposed method is suitable and effective for the decision process to evaluate their best alternative.     

A Fault Diagnosis Method Considering Meteorological Factors for Transmission Networks Based on P Systems

Bad meteorological conditions may reduce the reliability of power communication equipment, which can increase the distortion possibility of fault information in the communication process, hence raising its uncertainty and incompleteness. To address the issue, this paper proposes a fault diagnosis method for transmission networks considering meteorological factors. Firstly, a spiking neural P system considering a meteorological living environment and its matrix reasoning algorithm are designed. Secondly, based on the topology structure of the target power transmission network and the action logic of its protection devices, a diagnosis model based on the spiking neural P system considering the meteorological living environment is built for each suspicious fault transmission line. Following this, the action messages of protection devices and corresponding temporal order information are used to obtain initial pulse values of input neurons of the diagnosis model, which are then modified with the gray fuzzy theory. Finally, the matrix reasoning algorithm of each model is executed in a parallel manner to obtain diagnosis results. Experiment results achieved out on IEEE 39-bus system show the feasibility and effectiveness of the proposed method.