时间序列模糊关联规则的挖掘

对于许多复杂系统产生的时间序列,研究序列的局部行为和局部关联特征往往比原来的研究系统全局性模型具有明显的优势。为研究时间序列内部或时间序列间局部形态的关联特征,文章借助模糊集来软化时间序列属性论域的划分边界从而研究时间序列局部形态的模糊关联规则、规则可信度和规则的评价方法。实际算例显示了算法的有效性。     

Automatic localization of craniofacial landmarks for assisted cephalometry

In this paper we propose a system for localization of cephalometric landmarks. The process of localization is carried out in two steps: deriving a smaller expectation window for each landmark using a trained neuro-fuzzy system (NFS) then applying a template-matching algorithm to pin point the exact location of the landmark. Four points are located on each image using edge detection. The four points are used to extract more features such as distances, shifts and rotation angles of the skull. Limited numbers of representative groups that will be used for training are selected based on k-means clustering. The most effective features are selected based on a Fisher discriminant for each feature set. Using fuzzy linguistics if-then rules, membership degree is assigned to each of the selected features and fed to the FNS. The FNS is trained, utilizing gradient descent, to learn the relation between the sizes, rotations and translations of landmarks and their locations. The data for training is obtained manually from one image from each cluster. Images whose features are located closer to the center of their cluster are used for extracting data for the training set. The expected locations on target images can then be predicted using the trained FNS. For each landmark a parametric template space is constructed from a set of templates extracted from several images based on the clarity of the landmark in that image. The template is matched to the search windows to find the exact location of the landmark. Decomposition of landmark shapes is used to desensitize the algorithm to size differences. The system is trained to locate 20 landmarks on a database of 565 images. Preliminary results show a recognition rate of more than 90%.     

Adaptive fuzzy inference neural network

An adaptive fuzzy inference neural network (AFINN) is proposed in this paper. It has self-construction ability, parameter estimation ability and rule extraction ability. The structure of AFINN is formed by the following four phases: (1) initial rule creation, (2) selection of important input elements, (3) identification of the network structure and (4) parameter estimation using LMS (least-mean square) algorithm. When the number of input dimension is large, the conventional fuzzy systems often cannot handle the task correctly because the degree of each rule becomes too small. AFINN solves such a problem by modification of the learning and inference algorithm.     

模糊PID自适应控制器的应用设计

本文将模糊系统与传统PID控制相结合，设计一种模糊PID自适应控制器，这种控制器以误差e（k）和误差变化率ec（k）作为输入，可以满足不同时刻的e（k）和ec（k）对PID参数自整定的要求。将该控制器应用负反馈单闭环直流调速系统中，经仿真验证模糊PID自适应控制器不但具有传统PID控制精度高的优点，又兼有模糊控制灵活、适应性强的优点，保证了系统具有良好的动、稳态特性。     

包钢连铸结晶器液位控制系统中控制器的设计应用

本文采用FUZZY－PID复合式自适应控制器来满足冶金连铸系统中对结晶器液位控制的要求，由于在长时间的连续铸钢过程中，塞棒头部受钢水的冲刷和腐蚀，使得塞棒头部为形较大，导致塞棒位置与钢水流量的特性关系发生较大变化，而这种变化无法用确定的数学关系式来描述，用常规PID控制器不能对此变化进行有效调节。本文采用模糊控制器对此变化作出反映，通过模糊控制运算结果来调节PID控制器的放大系数Kp始终在理想的范围内，从而使控制系统达到对结晶器液位控制的要求。     

模糊参数桁架结构的有限元分析及广义可靠度计算

研究了模糊参数杆系结构在模糊荷载作用下的有限元分析和广义可靠度计算问题。提出了从模糊变量中提取模糊因子,在结构的模糊位移和应力响应中确定其隶属度函数的处理方法;导出了模糊失效准则下的结构广义可靠度计算公式。计算结果表明:文中提出的方法和计算公式是可行有效的。     

基于ADuC824的模糊石油闪点测定仪

主要论述了利用美国AD公司的最新微芯片ADuC824和模糊控制技术设计的单片机模糊炉温控制系统，并将其应用于石油产品的闪点自动测试过程中。实践证明，该系统比传统的微芯片和PID调节控制系统具有实时性强，精度高和误差小等明显优点。     

压铸镁合金选择的模糊综合评判方法

合理地选择合金是进行压铸产品设计的重要环节之一。本文基于模糊综合评判原理 ,建立了压铸镁合金材料选择系统 ,快捷有效地选择出最合适的合金牌号。同时 ,以一 MP3外壳选材为例 ,具体说明系统运行过程     

可变论域的模糊自整定控制方法

该文将模糊控制中的“可变论域”思想和参数模糊自整定方法相结合，提出一种“可变论域”的模糊自整定控制方法，并将它成功应用在转台的速率控制中，为模糊控制在工程中的应用提供了一种新的、简单易行的途径。     

基于模糊分析的机械强度可靠性方法

将机械零件的应力和强度均视为模糊变量 ,用模糊贴近度方法建立了机械零部件应力 -强度可靠性模型。该模型不限于应力和强度服从正态分布 ,便于工程应用 ,且能很好地处理离散数据的应力与强度的可靠性问题