广义正交多项式用于时变延时线性系统的参数辨识

应用广义正交多项式(GOP)的展开式估计时变延时线性系统的参数.其基本思想是状 态函数和控制函数分别用有限多项广义正交多项式表示,利用GOP的运算矩阵,将时变延时 微分方程转化为用展开系数表示的线性方程组,通过输入输出数据,参数能够辨识.     

一种新型的非参数模型及其在系统辨识中的应用

本文给出了一个求解广义正交多项式的微分运算矩阵的新方法，应用对连续线性系统的脉冲响应函数进行正交逼近的方法来讨论脉冲响应函数的实现问题，得到了一类新型的非参数模型，并导出了利用该模型来辨识连续线性系统的脉冲响应函数的算法，最后给出了例子证实本文所给方法的有效性。     

非线性时变大系统递阶控制的PGOPO法

提出了一类新型的线性、有界算子--分段广义正交多项式算子(PGOPO),建立了其 主要性质及运算规则;随后将PGOPO法用于求解非线性时变大系统最优控制问题.在这种新 型逼近运算中将PGOPO法和改进型关联预估法相结合,得出显式递阶递推算法更易于计算机 计算和推广.数值仿真实例说明了给出的算法是有效的.     

一类系统参数辨识的按段多重一般正交多项式方法

本文介绍了按段多重一般正交多项式系及其基本性质,并把它们应用于参数可分离系统的参数辨识.由于采用了按段低阶正交多项式多重逼近技术,该方法具有计算量少、结果精度高、可递推计算及不需要被辨识参数的初始估计等优点.本文提出了两个算法,并成功地应用于发酵过程细菌生长动力学模型的参数辨识.     

多元Chebyshev正交多项式混合模型及其在医学图像分割中的应用

针对原有一元正交多项式混合模型只能根据灰度特征分割图像的问题,提出一种基于多元Chebyshev正交 多项式混合模型的多维特征的医学图像分割方法。首先,根据Fouricr分析方法与张量积理论推导出图像的多元 Chcbyshcv正交多项式,并构建多元正交多项式的非参数混合模型,用最小均方差(MISE)估计每一个模型的平滑参 数;然后,用EM算法求解正交多项式系数和模型的混合比。此方法不需要对模型作任何假设,可以有效克服“模型失 配”问题。通过实验,表明了该分割方法的有效性。     

广义V-系统的构造及相应的快速变换

为了深入研究信号分析中有效的数学工具——正交函数和正交变换,从Legendre正交多项式出发,构造一类由分段多项式组成的正交函数系,称之为广义k次V-系统,并指出它与k次V-系统的等价关系.首先给出广义k次V-系统对应的离散矩阵,用于广义k次V-变换;然后证明了在保持V-变换的几乎全部特点的同时,广义V-变换还具有快速算法,弥补了V-系统不容易设计快速算法的缺憾.实验结果表明,快速广义V-变换比V-变换在时间效率上有明显提高.     

按段多重契比雪夫多项式系及其在线性时变系统辨识中的应用

本文在块脉冲函数系和契比雪夫多项式系基础上定义了一种新的正交函数系--按段多 重契比雪夫多项式系,研究该函数系的主要性质和基本运算法则,得出了积分运算矩阵、乘积 运算矩阵和元素乘积运算矩阵,并用此函数系研究线性时变系统的参数辨识问题,获得了简 单、快速、高精度的递推辨识算法.数值例子计算结果表明,当采用如伪随机信号一类的充分 激励的函数作为被辨识系统的试验信号,本文提出的算法所得结果的精度和计算时间都比一 般正交契比雪夫多项式算法所得结果为好.     

基于正交多项式旋转梁分布动载荷时域识别

引入二维广义正交多项式理论,以正交多项式序列作为基函数拟合分布动载荷,将面载的识别转化为正交多项式拟合系数的求解。针对弹性旋转梁的有限元模型,利用时域下的连续梁载荷识别理论去辨识未知分布力。数值算例表明:只要获得旋转梁上最够多的响应点信息,识别出的分布动载荷具有较高的精度;该识别方法简单有效,适合工程应用。     

广义正交模糊Maclaurin对称平均算子及其应用*

研究广义正交模糊决策环境下的集结算子及其决策应用。针对在信息集成时,需要考虑多个输入变量之间的相关关系以及专家的评价值为广义正交模糊信息的多属性决策问题,提出一种解决广义正交模糊多属性决策问题的方法。考虑到Maclaurin对称平均算子能够反映多个输入变量之间的相关关系,利用该算子集结广义正交模糊信息,提出了广义正交模糊Maclaurin对称平均算子、广义正交模糊加权Maclaurin对称平均算子,并研究了这些算子的性质和特殊情形。提出了基于广义正交模糊集结算子的多属性决策方法,并通过实例验证了其可行性和优势。     

正交U变换及其快速算法

采用递推方法构造一种正交变换,称之为U变换,该变换含有分段常数基向量、分段一次多项式基向量以及分段二次多项式基向量,是对Walsh变换、斜变换的推广。根据递推方式,可以得到相应的快速算法。利用平移复制算子和Kronecher积的性质,推导基于Kronecher积的快速算法和正交U变换的直接分解算法。将该变换应用于图像压缩中,构造基于人类视觉系统的量化表,实验结果表明,正交U变换的图像压缩性能明显优于斜变换的图像压缩性能,与DCT变换的图像压缩性能相当,为图像压缩提供了一种新的选择。     

Identification of nonlinear systems via piecewise general orthogonal polynomials operator

Based on a hybrid orthogonal function, a new linear, continuous and bounded operator, piecewise general orthogonal polynomials operator (PGOPO), is proposed. Its main properties and operational rules have been strictly constructed based on the theory of convergence in the mean square, and are useful to discuss the orthogonal polynomial approach in the proper mathematics frame. Then applying the PGOPO method to solve the identification problem of nonlinear systems, the convergence analysis of PGOPO approximation method is given. Finally, using the PGOPO method to solve the parameter identification of two kinds of time-varying systems, the simulation studies show that the algorithm is simple and more effective than that of general orthogonal polynomials.     

Solution of two-point-boundary-value problems by generalized orthogonal polynomials and application to optimal control of lumped and distributed parameter systems

A set of generalized orthogonal polynomials (GOPs) that can represent all types of orthogonal polynomial and non-orthogonal Taylor series are first introduced to solve dynamic state equations with two-point-boundary conditions. The basic idea is that any orthogonal polynomial function can be expressed as a power series, and vice versa. The operational matrix for the integration of the generalized orthogonal polynomials is thus derived. Using the special characteristics of these generalized orthogonal polynomials, the state equation of the two-point-boundary-value problem is thus reduced to that of an initial-value problem. This effective approach can be applied to solve the optimal control of a lumped or distributed parameter system. The computational algorithm, in conjunction with the recursive formula, is much simpler and easier than that for conventional individual orthogonal polynomials.     

New approach for parameter identification via generalized orthogonal polynomials

An effective method is presented for using generalized orthogonal polynomials (GOP) for identifying the parameters of a process whose behaviour can be modelled by a linear differential equation with time-invariant coefficients. The method is based on the differentiation operational matrix of the GOP, which can represent all kinds of individual orthogonal polynomials. The main advantage of using the differentiation operational matrix is that parameter estimation can be made starting at any time of interest, without the restriction of starting at zero time. Using the concept of GOP expansion for a state function and a control function, the differential input-output equation is converted into a set of over-determined linear algebraic equations. The unknown parameters are evaluated by a weighted least-squares estimation method. Two examples are given to demonstrate the validity of the method and good results are obtained.     

A new approach for parameter identification of time-varying systems via generalized orthogonal polynomials

A very effective method of using the generalized orthogonal polynomials (GOP) for identifying the parameters of a process whose behaviour can be modelled by a linear differential equation with time-varying coefficients in the form of finite-order polynomials is presented. It is based on the differentiation operational matrix of the GOP, which can represent all kinds of individual orthogonal polynomials. The main advantage of using the differentiation operational matrix is that parameter estimation can be made starting at any time of interest, i.e. without the restriction of starting at zero time. In addition, the present computation algorithm is simpler than that of the integration operational matrix. Using the concept of GOP expansion for state and control functions, the differential input-output equation is converted into a set of linear algebraic equations. The unknown parameters are evaluated by a weighted least-squares estimation method. Very satisfactory results for illustrative example are obtained.     

Partial Differential Equations and Bivariate Orthogonal Polynomials

In 1929, S. Bochner identified the families of polynomials which are eigenfunctions of a second-order linear differential operator. What is the appropriate generalization of this result to bivariate polynomials? One approach, due to Krall and Sheffer in 1967 and pursued by others, is to determine which linear partial differential operators have orthogonal polynomial solutions with all the polynomials in the family of the same degree sharing the same eigenvalue. In fact, such an operator only determines a multi-dimensional eigenspace associated with each eigenvalue; it does not determine the individual polynomials, even up to a multiplicative constant. In contrast, our approach is to seek pairs of linear differential operators which have joint eigenfunctions that comprise a family of bivariate orthogonal polynomials. This approach entails the addition of some “normalizing" or “regularity" conditions which allow determination of a unique family of orthogonal polynomials. In this article we formulate and solve such a problem and show with the help of Mathematica that the only solutions are disk polynomials. Applications are given to product formulas and hypergroup measure algebras.     

Evaluation of spline functions for digital filtering problems

Methods for the evaluation of spline functions for digital filtering in data processing systems are developed. Basis polynomials of general form and basis discrete orthogonal polynomials are considered. Computations are organized by solving constrained optimization problems. Recurrences for the system of normalized discrete orthogonal polynomials and their derivatives are obtained. The proposed spline functions on discrete orthogonal polynomials reduce the computational cost and approximation errors compared with the case of general polynomials. The results of the statistical simulation of the application of spline functions based on discrete orthogonal polynomials in digital filtering problems are presented.     

Application of general discrete orthogonal polynomials to optimal-control systems

The shift-transformation matrix of general discrete orthogonal polynomials is introduced. General discrete orthogonal polynomials are adopted to obtain the modified discrete Euler-Lagrange equations. Then general discrete orthogonal polynomials are applied to simplify the discrete Euler-Lagrange equations into a set of linear algebraic ones for the approximation of state and control variables of digital systems. An example is included to demonstrate the simplicity and applicability of the method. Also, a comparison of the results obtained via several classical discrete orthogonal polynomials for the same problem is given.     

Solutions of convolution integral and integral equations via double general orthogonal polynomials

Double general orthogonal polynomials are developed in this work to approximate the solutions of convolution integrals, Volterra integral equations, and Fredholm integral equations. The proposed method reduces the computations of integral equations to the successive solution of a set of linear algebraic equations in matrix form; thus, the computational complexity is considerably simplified. Furthermore, the solutions obtained by the general orthogonal polynomials include as special cases solutions by Chebyshev polynomials, Legendre polynomials, Laguerre polynomials, or Jacobi polynomials. A comparison of the results obtained via several different classical orthogonal polynomial approximations is also presented.