正则模糊神经网络对一类连续模糊值函数的普遍近似性

本文讨论了正则模糊神经网络于对定义于区间［０，Ｔ０］上连续可减模糊值函数的普遍近似性，在此基础上，证明了［０，Ｔ０］上取值为三角形模糊连续递增函数可用正则模糊神经网络逼近到任意精度。     

基于智能模糊控制策略的有源消声

有源消声是一种有效的低频噪声控制方法，由于空间有源消声过程具有时变性和非线性，传统的自适应算法不能进行很有效的控制。本文采用智能模糊控制策略，建立了基于智能模糊控制策略的有源消声系统，通过实验与传统自适应控制算法的消声效果作对比分析，证明智能模糊控制器不但控制速度快，易于实现，而且消声效果明显优于后者，具有较大的应用推广价值。     

Nonlinear black-box modeling in system identification: a unified overview

A nonlinear black-box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics. There has been considerable recent interest in this area, with structures based on neural networks, radial basis networks, wavelet networks and hinging hyperplanes, as well as wavelet-transform-based methods and models based on fuzzy sets and fuzzy rules. This paper describes all these approaches in a common framework, from a user's perspective. It focuses on what are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques. It is pointed out that the nonlinear structures can be seen as a concatenation of a mapping form observed data to a regression vector and a nonlinear mapping from the regressor space to the output space. These mappings are discussed separately. The latter mapping is usually formed as a basis function expansion. The basis functions are typically formed from one simple scalar function, which is modified in terms of scale and location. The expansion from the scalar argument to the regressor space is achieved by a radial- or a ridge-type approach. Basic techniques for estimating the parameters in the structures are criterion minimization, as well as two-step procedures, where first the relevant basis functions are determined, using data, and then a linear least-squares step to determine the coordinates of the function approximation. A particular problem is to deal with the large number of potentially necessary parameters. This is handled by making the number of ‘used’ parameters considerably less than the number of ‘offered’ parameters, by regularization, shrinking, pruning or regressor selection.     

电梯交通系统(Ⅰ)──电梯交通配置现状及发展

本文对国内外电梯交通配置设计现状及发展趋势作了论述。指出了由统计特性描述进入到采用专家系统和模糊规则的动态特性描述这一总的发展趋势。     

螺杆泵举升性能模糊综合评价

对螺杆泵举升性能的影响因素进行了综合分析，建立了螺杆泵举升性能综合评价的指标体系，并利用模糊数学方法确立了该系统的综合评价模型，将该模型应用于2台GLB500—12型螺杆泵，其评价结果与在实际工作中的产液量和寿命的综合指标基本吻合，从而验证了该方法的正确性，为螺杆泵举升性能的综合评价提供了一种有效方法。     

一种基于模糊集的医学图像增强方法

鉴于医学超声图像的对比度较差，使得某些图像细节难以辨认，采取基于小波包和模糊集的图像增强方法。结果表明，此增强方法能够增强图像微小细节，提高图像对比度，改善图像的视觉效果，从而提高诊断的准确性。     

模糊技术的发展及在相关领域中的应用

本文介绍了模糊技术的发展和现状，以及在相关领域中的应用情况，并且讨论了模糊技术在我国的产业化方向等问题。     

利用模糊评判方法确定张东构造油藏储量计算的可靠程度

张巨河油田张东构造地处大港滩海交接处,海域部分探井少.目前用油藏描述方法计算的探明储量,其落实程度备受关注.储量落实程度的评价是一个多因素综合评判问题,而且这些因素还分属不同层次.模糊综合评判是应用较为广泛的模糊数学方法之一.该方法建立的综合评判涉及三个要素,即因素集、评判集和单因素评判,由此所构造的模糊评判关系是模糊综合评判的基础.本文用该方法对张东构造沙二段油藏的探明储量落实程度作了尝试性评价.     

A design and analysis of objective function-based unsupervised neural networks for fuzzy clustering

Fuzzy clustering has played an important role in solving many problems. In this paper, we design an unsupervised neural network model based on a fuzzy objective function, called OFUNN. The learning rule for the OFUNN model is a result of the formal derivation by the gradient descent method of a fuzzy objective function. The performance of the cluster analysis algorithm is often evaluated by counting the number of crisp clustering errors. However, the number of clustering errors alone is not a reliable and consistent measure for the performance of clustering, especially in the case of input data with fuzzy boundaries. We introduce two measures to evaluate the performance of the fuzzy clustering algorithm. The clustering results on three data sets, Iris data and two artificial data sets, are analyzed using the proposed measures. They show that OFUNN is very competitive in terms of speed and accuracy compared to the fuzzy c-means algorithm.     

EXTENSION OF THE RELATIONAL DATABASE AND ITS ALGEBRA WITH ROUGH SET TECHNIQUES

This paper describes a database model based on the original rough sets theory. Its rough relations permit the representation of a rough set of tuples not definable in terms of the elementary classes, except through use of lower and upper approximations. The rough relational database model also incorporates indiscernibility in the representation and in all the operators of the rough relational algebra. This indiscernibility is based strictly on equivalence classes which must be defined for every attribute domain. There are several obvious applications for which the rough relational database model can more accurately model an enterprise than does the standard relational model. These include systems involving ambiguous, imprecise, or uncertain data. Retrieval over mismatched domains caused by the merging of one or more applications can be facilitated by the use of indiscernibility, and naive system users can achieve greater recall with the rough relational database. In addition, applications inherently “rough” could be more easily implemented and maintained in the rough relational database.