On-line estimation of the parameters of a multivariable system using matrix pseudo-inverse

A recursive pseudo-inverse algorithm is presented for estimating the parameters of the transfer-function matrix of a multiple-input-multiple-output discrete-time system from the samples of the input-output data. The algorithm is suitable for on-line identification of linear multivariable systems.     

The intensity of energy for consumption investigated with input — output models

On the basis of input-output models coefficients of energy intensity for consumption and for some complexes of needs have been calculated and investigated for a period of 10 years. Despite of the existent inaccuracies these coefficients permit to calculate the amount of energy demanded in regard to the further shaping of the socialist way of life. By that,it is possible to create a methodical enlargement for prognostic investigations because these coefficients can be estimated outside of the input-output model for instance with econometric functions, valuations by experts and others.     

Minimal dimension realization and identifiability of input-output sequences

A necessary and sufficient condition of identifiability of an input-output sequence is given. An algorithm is presented by which a minimal dimension realization from a given input-output sequence can be obtained. A canonical input-output equation is introduced. It is shown that the state equation realization from a canonical input-output equation exists and is unique, up to an isomorphism.     

基于动态线性化的非线性系统的自适应预测函数控制

基于pseudo-partia l-derivative(PPD)的概念动态线性化非线性系统,利用集结方法处理未来预测时刻的PPD,实现了非线性系统的自适应预测函数控制.所给算法的预测模型只与当前时刻的测量数据有关,不依赖于对象的具体结构.该算法能够提供有界的输入输出,并能无偏差跟踪给定值.最后通过大滞后对象和强非线性pH中和滴定实验验证了该方法的有效性,并说明了其具有很强的鲁棒性和抗干扰能力.     

Input-output block decoupling of linear time-varying singularsystems

The input-output block decoupling problem by state feedback is studied for linear time-varying singular systems. First, an algorithm, the regularization algorithm, is developed such that the system can be made by state feedback to have a unique impulse-free solution. Second, another algorithm, the block decoupling algorithm, is proposed, which provides sufficient conditions for the solvability of the input-output block decoupling problem. Then, a decoupling feedback law is constructed such that the corresponding closed-loop system is regular, impulse-free, and noninteractive. Finally, an example is given to illustrate the applicability of the algorithms     

基于双边转移概率矩阵的JPEG图像隐写检测

针对当前基于JPEG图像的典型隐写算法,基于离散余弦变换(DCT)域分块内及分块间相邻系数之间的相关性进行分析,通过将相邻三个系数的中间位置的系数作为条件,统计两边系数的条件分布概率矩阵,将提取的所得矩阵作为隐写检测敏感特征,提出了一种基于DCT系数双边转移概率分布的JPEG图像隐写检测算法。实验结果表明,在不同嵌入率下,该算法的检测性能均优于已有检测算法。     

Multivariable system structure and parameter identification using the correlation method

This paper describes an algorithm for the structure determination and parameter identification of linear discrete-time multivariable systems from input-output measurements. The algorithm starts by determining the structure parameters of a certain canonical state space representation from an estimate of the correlation functions of the output sequence. Then the parameters of the A matrix are estimated from the estimated correlation functions using the recursive least squares method. Finally a normalized stochastic approximation algorithm is used for the estimation of the parameters of the B matrix from input-output measurements.     

Evaluation of matrix polynomials in the state companion matrix of linear time invariant systems

In this paper a new iterative algorithm is presented for the numerical evaluation of matrix polynomials in the state companion matrix of a linear time invariant system. The algorithm also enables the polynomial coefficients of the characteristic equation and the eigenvectors of the state companion matrix to be found. In particular, a truncated series approximation is given for the state transition matrix, providing the most efficient computation known for a prescribed truncation error. The adjustment recursive coefficients used in evaluating the state transition matrix are shown to be on alternative means of evaluating the elements of the inverse Vandermonde matrix of the systems' eigenvalues.     

Input-output linearizing control of constrained nonlinear processes

An input-output linearization strategy for constrained nonlinear processes is proposed. The system may have constraints on both the manipulated input and the controlled output. The nonlinear control system is comprised of: (i) an input-output linearizing controller that compensates for processes nonlinearities; (ii) a constraint mapping algorithm that transforms the original input constraints into constraints on the manipulated input of the feedback linearized system; (iii) a linear model predictive controller that regulates the resulting constrained linear system; and (iv) a disturbance model that ensures offset-free setpoint tracking. As a result of these features, the approach combines the computational simplicity of input output linearization and the constraint handling capability of model predictive control. Simulation results for a continuous stirred tank reactor demonstrate the superior performance of the proposed strategy as compared to conventional input-output linearizing control and model predictive control techniques.     

基于小波包分解与矩阵编码的自适应音频隐写算法

针对音频隐写算法载体利用率较低、不可感知性较差、嵌入效率不高的问题,提出了一种基于小波包分解与矩阵编码的自适应音频隐写算法。算法通过对比音频文件在MP3压缩前后小波包分解系数上的差异,将小波系数不变的位数作为嵌入载体,有效提高了嵌入容量;另外采用混沌模型产生随机三元组对矩阵编码算法进行改进,提高了算法的安全性与嵌入效率。算法在容量方面较直接选取中频子带小波系数作为嵌入载体的隐写算法提升约30%;信噪比(SNR)较使用固定三元组的矩阵编码隐写算法提升约9%。实验结果证明了算法的正确性,能基本满足大容量安全通信的需要。     

一种多层前馈网参数可分离学习算法

目前大部分神经网络学习算法都是对网络所有的参数同时进行学习.当网络规模较 大时,这种做法常常很耗时.由于许多网络,例如感知器、径向基函数网络、概率广义回归网络 以及模糊神经网络,都是一种多层前馈型网络,它们的输入输出映射都可以表示为一组可变 基的线性组合.网络的参数也表现为二类:可变基中的参数是非线性的,组合系数是线性的. 为此,提出了一个将这二类参数进行分离学习的算法.仿真结果表明,这个学习算法加快了学 习过程,提高了网络的逼近性能.     

A sufficient condition for the stability of matrix polynomials

A sufficient condition is presented for the stability of the matrix polynomials based on algebraic properties of the matrix coefficients. The stability condition is derived from the Lyapunov theory by a multivariable feedback system that is associated to the matrix polynomial. Illustrative examples are given. A block-Schwarz form related to the matrix polynomial is obtained directly from the given realization algorithm     

A general approach to input-output pseudolinearization fornonlinear systems

A nonlinear system is said to be input-output pseudolinearized if its family of linearizations about constant operating points has input-output behavior that is independent of the particular operating point. The problem of constructing static state feedback laws that achieve input-output pseudolinearization for a general class of nonlinear systems is considered. A generalization of the well-known structure algorithm to the case of parameterized families of linear systems plays an important role     

Subspace model identification Part 1. The output-error state-space model identification class of algorithms

In this paper, we present two novel algorithms to realize a finite dimensional, linear time-invariant state-space model from input-output data. The algorithms have a number of common features. They are classified as one of the subspace model identification schemes, in that a major part of the identification problem consists of calculating specially structured subspaces of spaces defined by the input-output data. This structure is then exploited in the calculation of a realization. Another common feature is their algorithmic organization: an RQ factorization followed by a singular value decomposition and the solution of an overdetermined set (or sets) of equations. The schemes assume that the underlying system has an output-error structure and that a measurable input sequence is available. The latter characteristic indicates that both schemes are versions of the MIMO Output-Error State Space model identification (MOESP) approach. The first algorithm is denoted in particular as the (elementary MOESP scheme). The subspace approximation step requires, in addition to input-output data, knowledge of a restricted set of Markov parameters. The second algorithm, referred to as the (ordinary MOESP scheme), solely relies on input-output data. A compact implementation is presented of both schemes. Although we restrict our presentation here to error-free input-output data, a framework is set up in an identification context. The identification aspects of the presented realization schemes are treated in the forthcoming Parts 2 and 3.     

‘I-O-W’ model for national planning

The paper proposes an input-output-waste (I-O-W) model as an extension of the input-output model to incorporate waste management (WM) aspects in national planning. The waste aspects have not been explicitly considered in the existing literature on input-output economics. It is visualized that in the economic structure the waste parameter has a significant role to play, and the I-O-W model will be a closer representation of the economy than that of conventional input-output models. Any unnecessary input to or any undesirable output from the system is considered as waste. The basic framework of an I-O-W model is presented, which consists of a technology matrix, a final-demand matrix, and a WM policy matrix. The resource balance has been established by treating the input to be equal to the sum of output and waste. The technological coefficients are interlinked by wastivity indices. The projections of a hypothetical economy consisting of one production sector and one WM sector have been determined for material resources and for different WM policies.     

Syntax-directed,semantics-supported program synthesis

A number of strategies for the synthesis of algorithms from a given input-output specification of a problem are presented which are centered around a few basic principles. It has been verified for more than ten different algorithms that their uniform application in all cases results in a successful deductive synthesis. Many of these deductions are presented here including those of a spanning-tree algorithm, a graph-circuits algorithm, a finding the ith smallest element algorithm, and a linear (string) pattern-matching algorithm.     

一种基于Off-Policy的无模型输出数据反馈H∞ 控制方法

针对模型未知的线性离散系统在扰动存在条件下的调节控制问题, 提出了一种基于Off-policy的输入输出数据反馈的H_∞控制方法. 本文从状态反馈在线学习算法出发, 针对系统运行过程中状态数据难以测得的问题, 通过引入增广数据向量将状态反馈策略迭代在线学习算法转化为输入输出数据反馈在线学习算法. 更进一步, 通过引入辅助项的方法将输入输出数据反馈策略迭代在线学习算法转化为无模型输入输出数据反馈Off-policy学习算法. 该算法利用历史输入输出数据实现最优输出反馈策略的学习, 同时克服了On-policy算法需要频繁与实际环境进行交互这一缺点. 除此之外, 与On-policy算法相比, Off-policy学习算法具有克服学习噪声的影响, 使学习结果收敛于理论最优值这一优点. 最终, 通过仿真实验验证了学习算法的收敛性.     

Minimal representations of MIMO time-varying systems and realization of cyclostationary models

This paper describes a family of minimally parameterized state-space models for completely n-step observable multi-input multi-output time-varying systems and their algebraical links with equivalent input-output models. These results are then used to develop an algorithm for the realization of generic input-output sequences generated by a periodic system. A complete numerical example is also developed to illustrate all steps of the algorithm described in the paper.