摘要:

This paper examines the problem of parameter estimation for auto-regressive (AR) models with missing observations. The pattern for the missing observations is arbitrary. Various method are proposed and simulation results are presented.

ISSN:0020-7721    

DOI:10.1080/00207728008967071

出版商:Taylor & Francis Group    

年代:1980

数据来源: Taylor

摘要:

In this paper a finite-dimensional model is considered which approximates the one-dimensional diffusion process with pointwise observation and controls. The structural properties of the model are investigated, and sufficient conditions for its global identifiability are stated. These involve the input and output matrices of the model and are related to the location of observation and control points. Then the problem of identifying the parameters of the model from the inputs-output data of the distributed system is investigated and the uniqueness of the estimate is discussod. Finally numerical applications are reported. Though the approach is deterministic, the effect of random output noise is tested in the numerical applications.

ISSN:0020-7721    

DOI:10.1080/00207728008967072

出版商:Taylor & Francis Group    

年代:1980

数据来源: Taylor

摘要:

In this paper, the class of linear multivariable continuous-time plants which are amenable to fast-sampling decentralized error-actuated control is characterized in terms of the relatively prime polynomial matrix factors of the transfer function matrices of such plants. It is shown that this characterization greatly facilitates the design of fast-sampling decentralized error-actuated controllers, and the resulting synthesis technique is illustrated by designing such a controller for a third-order plant. with two inputs and two outputs.

ISSN:0020-7721    

DOI:10.1080/00207728008967073

出版商:Taylor & Francis Group    

年代:1980

数据来源: Taylor

摘要:

The aim of this paper is to study controllability for systems described by first-order evolution equations and for systems described by second-order evolution equations in the case of discrete-time controls. For systems with finite-dimensional controls we present necessary and sufficient conditions for controllability. We show that distributed parameter systems are never finite-step controllable. We also investigate the relations between systems with discrete-time controls and systems with continuous -time controls from the view-point of controllability. Moreover we see the essential difference between the parabolic case and the hyperbolic case.

ISSN:0020-7721    

DOI:10.1080/00207728008967074

出版商:Taylor & Francis Group    

年代:1980

数据来源: Taylor

摘要:

This paper is concerned with numerical techniques of solving a class of stochastic optimal control problem with the performance criterion taken as the probability of hitting a target. The system under consideration is governed by an I to stochastic differential equation. Such problems have been considered by Katayama and also by Van Mellaert and Dorato. However, they assumed that the controluappears linearly in the system equations. In addition, Katayama only computed time-invariant controls, while Van Mellaert and Dorato investigated a special case where the control directly affects only one component of the state variable. This paper presents a technique that works even if the control appears non-linearly in the system equations.

ISSN:0020-7721    

DOI:10.1080/00207728008967075

出版商:Taylor & Francis Group    

年代:1980

数据来源: Taylor

摘要:

This paper presents a method for the design of an observer capable of reconstucting several linear functionals of the states of a linear, finite-dimensional system. The goal of this method is the design of an observer having minimum order subject to the restriction that the observer eigenvalues be freely assignable. The method is based on the reduction of a state observer formulated from an observable canonic form for the system when the functionals are treated as if they were additional outputs.

ISSN:0020-7721    

DOI:10.1080/00207728008967076

出版商:Taylor & Francis Group    

年代:1980

数据来源: Taylor

摘要:

In this paper a revised GMDH (Group Method of Data Handling) algorithm is developed in whichheuristicsare not required such as dividing the available date. into training data and checking data, predetermining the structure of the partial polynomials, or predetermining the number of intermediate variables. In this algorithm the prediction error criterion, such as PSS (Prediction Sum of Squares) or AIC (Akaike's Information Criterion) evaluated from all the available data, in used as a criterion for generating optimal partial polynomials, for selecting intermediate variables and for stopping the multilayered iterative computation. Thisheuristics freeGMDH algorithm is applied to non-linear modelling for short-term prediction of air pollution concentration. By using the time series data of SO2, concentration, the wind velocity and the wind direction in Tokushima; Japan, a suitable model for predicting SO2concentration at a few hours in advance is developed. The predicted results obtained by the revised GMDH model are compared with the results obtained by a linear regression model, a linear autoregressive model and a. basic GMDH model.

ISSN:0020-7721    

DOI:10.1080/00207728008967077

出版商:Taylor & Francis Group    

年代:1980

数据来源: Taylor

摘要:

This paper considers the estimation problem for non-linear distributed-parameter systems via the ‘Partition Theorem’. First, the aposterioriprobability for the state is derived for the estimation of non-linear distributed-parameter systems. Secondly, linear systems excited by a white gaussian noise and with non-gaussian initial state are considered as a special class of the problem. Thea posterioriprobability for the state, the optimal estimates and corresponding error covariance matrices are obtained by using the properties of the fundamental solution for the differential operator. Finally, it is shown that on approximate expression for the solution of the problem is also derived by applying a gaussian sum approximation technique.

ISSN:0020-7721    

DOI:10.1080/00207728008967078

出版商:Taylor & Francis Group    

年代:1980

数据来源: Taylor