使用新混合模糊聚类算法的模糊系统建模方法

为了进一步提高模糊系统建立模型的精度,提出一种新的模糊系统算法ANFIS-HC-QPSO：采用一种混合型模糊聚类算法来对模糊系统的输入空间进行划分,每一个聚类通过高斯函数的拟合产生一个隶属度函数,即完成ANFIS系统的前件参数--隶属度函数参数的初始识别,通过具有量子行为的粒子群算法QPSO与最小二乘法优化前件参数,直至达到停机条件,最终得到ANFIS的前件及后件参数,从而得到满意的模糊系统模型。实验表明,AN-FIS-HC-QPSO算法与传统算法相比,能在只需较少模糊规则的前提下就使模糊系统达到更高的精度。     

Zernike矩结合Sigmoid曲线拟合边缘检测方法

为了改进Zernike矩边缘检测方法对非直线边缘检测精度低的弱点,提出了一种改进的基于Zernike矩方法和Sigmoid拟合法的边缘检测方法。用Zernike矩边缘检测方法检测边缘,然后用Sigmoid拟合方法检测边缘,根据边缘类型对两种方法检测的边缘结果进行加权求和,所得结果为边缘位置。分别利用理想图像和实际工业CT图像进行了实验验证,实验结果表明对非直线边缘的检测,该方法检测精度相对于Zernike矩方法提高了一个数量级。     

模糊聚类在股票投资中的应用

聚类分析即将一组事物根据其性质上亲疏远近的程度进行分类,把性质相近的个体归为一类,使得同一类中的个体具有高度的同质性,不同类之间的个体具有高度的异质性。模糊聚类分析是现今模糊理论应用最广泛和最富成果的技术之一。阐述模糊聚类的理论,以部分石油股票为例,抽取影响石油股票收益因素的数据,利用最大最小法建立相似矩阵,用传递闭包法作出聚类分析,并进行总结。     

基于灰色模糊层次分析法的企业云服务安全评价模型

从企业实施云服务项目角度,总结得出影响云服务安全的四大因素,即平台设施、运行安全、运营管理以及法律法规因素。在此基础上构建企业云服务安全评价模型,并利用灰色模糊层次分析法（ GFAHP）对模型进行综合评价。最后通过一个实例进行具体阐明与评价。     

用模糊规划证明期望放出体的合理性

文章采用模糊规划的方法证明了期望放出体在放矿中的合理性,从而使放矿这个复杂的过程能方便地采用数学手段来处理。同时说明了如何在崩落法矿山利用期望体来解决实际生产中的放矿问题。     

An alignment-free fingerprint bio-cryptosystem based on modified Voronoi neighbor structures

Bio-cryptography is an emerging security technology which combines cryptography with biometrics. A good bio-cryptosystem is required to protect the privacy of the relevant biometric data as well as achieving high recognition accuracy. Fingerprints have been widely used in bio-cryptosystem design. However, fingerprint uncertainty caused by distortion and rotation during the image capturing process makes it difficult to achieve a high recognition rate in most bio-cryptographic systems. Moreover, most existing bio-cryptosystems rely on the accurate detection of singular points for fingerprint image pre-alignment, which is very hard to achieve, and the image rotation transformation during the alignment process can cause significant singular point deviation and minutiae changes. In this paper, by taking full advantage of local Voronoi neighbor structures (VNSs), e.g. local structural stability and distortion insensitivity, we propose an alignment-free bio-cryptosystem based on fixed-length bit-string representations extracted from modified VNSs, which are rotation- and translation-invariant and distortion robust. The proposed alignment-free bio-cryptosystem is able to provide strong security while achieving good recognition performance. Experimental results in comparison with most existing alignment-free bio-cryptosystems using the publicly-available databases show the validity of the proposed scheme.     

Efficient multilevel image segmentation through fuzzy entropy maximization and graph cut optimization

The fuzzy c-partition entropy has been widely adopted as a global optimization technique for finding the optimized thresholds for multilevel image segmentation. However, it involves expensive computation as the number of thresholds increases and often yields noisy segmentation results since spatial coherence is not enforced. In this paper, an iterative calculation scheme is presented for reducing redundant computations in entropy evaluation. The efficiency of threshold selection is further improved through utilizing the artificial bee colony algorithm as the optimization technique. Finally, instead of performing thresholding for each pixel independently, the presented algorithm oversegments the input image into small regions and uses the probabilities of fuzzy events to define the costs of different label assignments for each region. The final segmentation results is computed using graph cut, which produces smooth segmentation results. The experimental results demonstrate the presented iterative calculation scheme can greatly reduce the running time and keep it stable as the number of required thresholds increases. Quantitative evaluations over 20 classic images also show that the presented algorithm outperforms existing multilevel segmentation approaches.     

A fuzzy neural network approach for online fault detection in waste water treatment process

In this paper, an effective strategy for fault detection of sludge volume index (SVI) sensor is proposed and tested on an experimental hardware setup in waste water treatment process (WWTP). The main objective of this fault detection strategy is to design a system which consists of the online sensors, the SVI predicting plant and fault diagnosis method. The SVI predicting plant is designed utilizing a fuzzy neural network (FNN), which is trained by a historical set of data collected during fault-free operation of WWTP. The fault diagnosis method, based on the difference between the measured concentration values and FNN predictions, allows a quick revealing of the faults. Then this proposed fault detection method is applied to a real WWTP and compared with other approaches. Experimental results show that the proposed fault detection strategy can obtain the fault signals of the SVI sensor online.     

On solutions to fuzzy stochastic differential equations with local martingales

This paper deals with a class of fuzzy stochastic differential equations (FSDEs) driven by a continuous local martingale under the Lipschitzian condition. Such equations can be useful in modeling hybrid systems, where the phenomena are simultaneously subjected to two kinds of uncertainties: randomness and fuzziness. The solutions of the FSDEs are the fuzzy stochastic processes, and their uniqueness is considered to be in a strong sense. Thus, the existence and uniqueness of solutions to the FSDEs under the Lipschitzian condition is first proven. Moreover, some asymptotic properties of the solutions to the FSDEs are investigated. Finally, an illustrating example on the interest term model is provided.     

基于不可分小波分解的图像配准方法

张量积小波强调的是图像中水平和垂直方向的高频信息,而不可分小波具有各向同性,可以提取图像中各个方向的边缘,能获得比较完整的图像轮廓,将这种特点应用于图像配准时,能准确定位图像仿射不变点的位置。为此,提出一种通过求取不可分小波分解后的高频子图像配准参数来配准原图像的方法,把图像的配准问题转化为其不可分小波分解后的高频子图像配准问题。从不可分小波分解的快速算法理论出发,证明该配准方法的正确性。构造一组四通道不可分小波滤波器组,在此基础上给出配准的方法和步骤。实验结果表明,该方法具有较好的配准效果,其求取图像配准参数的运算量比直接求取原图像配准参数运算量的1/4还少,与基于张量积小波分解的图像配准方法相比,具有较高的配准精度。