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.     

Performance analysis of the forgetting factor RLS algorithm

An analysis is given of the performance of the standard forgetting factor recursive least squares (RLS) algorithm when used for tracking time-varying linear regression models. Three basic results are obtained: (1) the ‘P-matrix’ in the algorithm remains bounded if and only if the (time-varying) covariance matrix of the regressors is uniformly non-singular; (2) if so, the parameter tracking error covariance matrix is of the order O(μ + γ²/μ), where μ = 1 - λ, λ is the forgetting factor and γ is a quantity reflecting the speed of the parameter variations; (3) this covariance matrix can be arbitrarily well approximated (for small enough μ) by an expression that is easy to compute.     

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.     

Identification of Hammerstein systems with asymmetric dead-zone nonlinearities using canonical parameterized model

A non-iterative identification method with parameterization of the unknown dead-zone is proposed for Hammerstein systems in presence of asymmetric dead-zone nonlinearities.The canonical parameterized model which is a single expression without segmentation is utilized to describe the dead-zone,based on which a universal-type parametric model can be established to approximate the entire system.This model can be established without separating the nonlinear part from the linear part.The dead-zone parameters and the coefficients in the linear transfer function can be estimated simultaneously according to the proposed algorithm.Numerical experiments are presented to illustrate the effectiveness of the proposed scheme.     

模糊自适应的高超飞行器控制与干扰

针对具有超大飞行马赫数、超宽飞行包络、飞行环境复杂等特点的高超声速飞行器,提出了基于模糊自适应的高超飞行器高度控制方法,分别设计了高度和速度控制器.针对所设计的控制器,分析了气动干扰力矩、测量噪声、舵机干扰、气动参数不确定性和飞行器模型参数不确定性对飞行器高度控制和速度控制效果的影响,然后又分析了综合考虑以上干扰和不确定性对飞行器高度控制和速度控制效果的影响.仿真结果表明,对各种干扰和不确定性,所设计的控制器速度和高度控制有较好的跟踪性和鲁棒性,达到了满意的效果.     

Adaptive fuzzy decentralized control for nonlinear large-scale systems based on high-gain observer

An adaptive fuzzy decentralized backstepping output-feedback control approach is proposed for a class of nonlinear large-scale systems with completely unknown functions,the interconnections mismatched in control inputs,and without the measurements of the states.Fuzzy logic systems are employed to approximate the unknown nonlinear functions,and an adaptive high-gain observer is developed to estimate the unmeasured states.Using the designed high-gain observer,and combining the fuzzy adaptive control theory with backstepping approach,an adaptive fuzzy decentralized backstepping output-feedback control scheme is developed.It is proved that the proposed control approach can guarantee that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded(SUUB),and that the observer errors and the tracking errors converge to a small neighborhood of the origin by appropriate choice of the design parameters.Finally,a simulation example is provided to show the eectiveness of the proposed approach.     

THz-TDS signal analysis and substance identification via the conformal split

A terahertz time-domain spectroscopy(THz-TDS)imaging system can obtain high-dimensional signals with substance fingerprint information.By introducing geometric algebra,a novel signal analysis approach to THz-TDS signals is developed based on an optical physical mechanism.Using this approach,signals are represented with vectors in the high-dimensional real vector space.Geometric distribution properties and algebraic relationships of THz-TDS signals are deduced.It is proved that every complex refractive index of substances relates to a unique 2-blade,the vectors corresponding to the samples of the same substance are collinear and belong to the intrinsic 2-blade of the substance.When decomposed through the conformal split with respect to a 2-blade,THz-TDS signals of high dimensionality can be related to vectors in a 2-dimensional subspace.Based on the conformal split properties we deduced,two criteria for substance identification on the basis of THz-TDS signals are proposed.Accordingly,a novel substance identification method via the conformal split is presented.In the method,the 2-blade related to each "known" substance is calculated with two vectors corresponding to THz-TDS signals measured from samples of the substance but with dierent thicknesses.Using the conformal split with respect to those 2-blades,an identified vector corresponding to a THz-TDS signal is linearly related to the vector in a 2-dimensional subspace.The substance of a sample can be identified using criteria on the projected vectors in the subspaces.This method can contribute to accurate classification and identification.Finally,two experiments are presented that show the feasibility and accuracy of this method.     

智能高压开关柜自动识别系统研究

开关柜误操作造成的事故越来越成为影响电力行业安全生产的主要事故类型之一。鉴于此,文章研究了一套智能高压开关柜自动识别系统。本系统中开关柜侧的装置在柜门被打开时可播放语音提示信息,并将开关柜开门关门等事件通过PT2262编码,再无线发送传输到后台。后台接收后,经F330单片机软件解码,并在触摸屏上记录事件的类型和时间。该系统对防止由于工作人员选错开关柜造成的误操作有一定的效果。     

基于指纹识别的虚拟硬盘研究

为防止机密数据不被窃取或篡改,本文提出一种基于指纹身份识别的虚拟硬盘文件加密存储方法。该方法是在Windows设备驱动程序的基础上,利用指纹识别技术和虚拟硬盘驱动程序,实现用户身份的唯一认证和文件的安全性管理。实验证明,该方法是一种安全、高效、实用的加密存储方法。     

水工钢闸门主梁模糊失效概率计算方法研究

为合理计算水工钢闸门主梁模糊失效概率,分别将主梁相对变形当作一个随机变量及三个变量的组合,采用积分法、当量随机化方法及蒙特卡罗法进行了计算。当相对变形为一个随机量时,采用积分法及当量随机化方法计算,两者的差别在于积分法用隶属函数描述模糊限值,当量随机化方法是将模糊限值当量作为一个随机量。当相对变形看作三个变量的组合时采用蒙特卡罗法进行计算,该方法考虑了三个变量的分布特性,更符合实际情况,模糊限值也用随机量表示。计算表明,积分法与当量随机化方法结果相近,验证了当量随机化方法的精度;蒙特卡罗法结果与相对变形服从正态分布时、用当量随机化方法计算的结果接近,故主梁相对变形服从正态分布更为合理。三种计算方法中,当量随机化方法计算失效概率相较于其它方法有计算过程简便,效率高的优点。