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。     

一种基于时间分层决策矩阵的综合评价算法

综合评价系统用四元总体（A，Z，X^(k)，D）表示，其中决策矩阵X^(k)是具有对象维i,指标维j,时间维k的三维矩阵，将三维矩阵X（k)按时间划分形成m个二维矩阵，每个二维矩阵分别代表每个年度n个评价对象对应于p个指标的属性集，讨论了按时间分层决策矩阵的多指标综合评价问题，描述了按时间分层的决策矩阵的求解算法，并采用Visual Foxpro数据库语言编程实现。     

加权算术平均与几何平均关系式的新证法

利用函数极小点的性质,得到了若干个正数的加权算术平均与几何平均关系式的新证法     

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。     

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.     

奇异值分解对非定常约束多体系统的动力学分析

通过对约束方程的雅可比矩阵进行奇异值分解，定义组合坐标，从拉格朗日第一类方程出发，建立了非定常约束多体系统的纯微分方程，给出了动力学数值分析的过程，最后举了一个算例。     

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.