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Automatic Control, IEEE Transactions on

Issue 6 • Date December 1974

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  • [Front cover and table of contents]

    Publication Year: 1974 , Page(s): 0
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  • Introduction

    Publication Year: 1974 , Page(s): 638 - 640
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  • [Back cover]

    Publication Year: 1974 , Page(s): 0
    Cited by:  Papers (3)
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  • Stochastic approximation methods for identification and control--A survey

    Publication Year: 1974 , Page(s): 798 - 809
    Cited by:  Papers (28)
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    Stochastic search techniques have been the essential part for most identification and self-organizing or learning control algorithms for stochastic systems. Stochastic approximation search algorithms have been very popular among the researchers in these areas because of their simplicity of implementation, convergence properties, as well as intuitive appeal to the investigator. This paper presents an exposition of the stochastic approximation algorithms and their application to various parameter identification and self-organizing control algorithms. View full abstract»

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  • III-posed and well-posed problems in systems identification

    Publication Year: 1974 , Page(s): 738 - 747
    Cited by:  Papers (8)
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    Impulse response identification almost always leads to an ill-posed mathematical problem. This fact is the basis for the well-known numerical difficulties of identification by means of the impulse response. The theory of regularizable ill-posed problems furnishes a unifying point of view for several specific methods of impulse response identification. In this paper we introduce a class of input/output representations, which we call λ-representations, for linear, time-invariant systems. For many cases of practical interest the identification of one of these representations is mathematically well-posed. Its determination is thus relatively insensitive to certain experimental uncertainties, and rational error-in-identification bounds may be found, so that λ-identification is often an attractive alternative to impulse response identification in the nonparametric modeling of physical systems which must be identified from input/ output records. We investigate the effects of input and output uncertainties (noise) on λ-identification, and discuss the problem of finding minimal realizations from these representations. We illustrate the work with an example of electromagnetic pulse (EMP) threat prediction using experimental data. Hard error bounds are provided on the predicted threat. For this problem, the appropriate λ-representation turns out to be the ramp response. View full abstract»

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  • A self-tuning predictor

    Publication Year: 1974 , Page(s): 848 - 851
    Cited by:  Papers (17)
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    An adaptive predictor for discrete time stochastic processes with constant but unknown parameters is described. The predictor which in real time tunes its parameters using the method of least squares is called a self-tuning predictor. The predictor has attractive asymptotic properties. If the parameter estimation converges and if the predictor contains parameters enough, then it will converge to the minimum square error predictor that could be obtained if the parameters of the process were known. The computations to be carried out at each sampling interval are very moderate and the algorithm is well suited for real-time applications. The self-tuning predictor can be used to predict processes which contain trends or periodic disturbances. Further, the algorithm can easily be modified in order to make it possible to predict processes with slowly time-varying parameters. View full abstract»

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  • Parameter estimation in the canine cardiovascular system

    Publication Year: 1974 , Page(s): 927 - 931
    Cited by:  Papers (3)
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    A new method of parameter estimation has been developed to estimate a newly chosen group of eight parameters of the canine arterial system. These parameters, which are physically meaningful, include arterial radii, Young's modulus for the aortic wall, aortic length, and peripheral resistances. The method is model-based, and depends upon minimization of a criterion which combines weighted integrals of absolute values of pressure and flow with errors in certain average and peak values of waveforms. A minimization algorithm which is a modification of the Hooke and Jeeves pattern search method was used. The entire scheme was implemented on a hybrid computer. This method of parameter estimation was developed and checked by use of model-to-model studies. Then dog-to-model parameter estimation runs were made, using data recorded in a dog experiment. Estimated parameters were found to check direct measurements where these were possible, and variations in estimated parameters from data obtained after various drug interventions were found to fall into expected patterns. Future applications of the methods developed would appear to have promise for obtaining rapid estimates of human parameters from noninvasive measurements made in clinical situations. View full abstract»

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  • Applications of principal component analysis and factor analysis in the identification of multivariable systems

    Publication Year: 1974 , Page(s): 730 - 734
    Cited by:  Papers (1)
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    The identification of a multivariable stochastic system, usually, involves the estimation of a transfer function matrix, which is a general function of frequency. This estimation involves inversion of a large Hermitian matrix, which sometimes may become unwieldly. In this paper we describe how "principal component analysis" in the frequency domain may be used to replace the input/output variables by some function of smaller dimensions without much "loss of information." The analogy between the "factor analysis" of time series in frequency domain and the minimal realization of state space models is pointed out. The principal component approach described in this paper is applied in the case of a simulated system. View full abstract»

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  • State-space models for infinite-dimensional systems

    Publication Year: 1974 , Page(s): 693 - 700
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (944 KB)  

    Distributed effects are present in almost all physical systems. In some cases these can be safely ignored but there are many interesting problems where these effects must be taken into account. Most infinite dimensional systems which are important in control theory are specifiable in terms of a finite number of parameters and hence are, in principle, amenable to identification. The state-space theory of infinite dimensional systems has advanced greatly in the last few years and is now at a point where real applications can be contemplated. The realizability criteria provided by this work can be employed effectively in the first step of the identification procedure, i.e., in the selection of an appropriate infinite dimensional model. We show that there exists a natural classification of nonrational transfer functions, which is based on the character of their singularities. This classification has important implications for the problem of finite dimensional approximations of infinite dimensional systems. In addition, it reveals the class of transfer functions for which there exist models with spectral properties closely reflecting the properties of the singularities of the transfer functions. The study of models with infinitesimal generators having a connected resolvent sheds light on some open problems in classical frequency response methods. Finally, the methods used here allow one to see the finite dimensional theory itself more clearly as the result of placing it in the context of a larger theory. View full abstract»

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  • Time series analysis and sleep research

    Publication Year: 1974 , Page(s): 932 - 943
    Cited by:  Papers (2)  |  Patents (1)
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    The characterization of biological data during sleepwaking state changes requires the utilization of time series and point process techniques of analysis. The time series procedures that have been found useful in describing biological activity during sleep include frequency domain techniques, such as the autospectrum, digital filtering, complex demodulation, coherence, and cross-spectrum calculations, as well as time domain procedures, such as autocorrelation and matched filtering. The point process techniques include interval distributions, intensity functions, serial correlations, and cross-intensity functions. In addition, applications of frequency domain techniques such as the interval spectrum and spectrum of counts have been implemented for point process data. View full abstract»

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  • Dynamic estimation of air pollution

    Publication Year: 1974 , Page(s): 904 - 910
    Cited by:  Papers (6)  |  Patents (1)
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    The advection-diffusion model of air pollution over an urban area is developed. The region is subdivided into a grid and the three-dimensional partial differential equation of the pollution concentration is reduced to a linear vector difference equation. Along with this discrete equation, a stochastic model of air pollution is considered and pollution concentrations over the area are estimated from observed data generated by a few monitor points. High dimensionality of the resulting Kalman filter equation is avoided by using a discrete form of Chandrasekar-type equations. Diffusion coefficients in the advection-diffusion model, which are difficult to specify accurately, are also identified. View full abstract»

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  • Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations

    Publication Year: 1974 , Page(s): 774 - 783
    Cited by:  Papers (43)
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    This paper discusses numerical aspects of computing maximum likelihood estimates for linear dynamical systems in state-vector form. Different gradient-based nonlinear programming methods are discussed in a unified framework and their applicability to maximum likelihood estimation is examined. The problems due to singular Hessian or singular information matrix that are common in practice are discussed in detail and methods for their solution are proposed. New results on the calculation of state sensitivity functions via reduced order models are given. Several methods for speeding convergence and reducing computation time are also discussed. View full abstract»

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  • Parameter estimation in multivariate stochastic difference equations

    Publication Year: 1974 , Page(s): 784 - 797
    Cited by:  Papers (14)
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    We will review the principal methods of estimation of parameters in multivariate autoregressive moving average equations which have additional observable input terms in them and present some new methods of estimation as well. We begin with the conditions for the estimability of the parameters. In addition to the usual method of system representation, the canonical form I, we will present two new representations of the system equation, the so-called canonical forms II and III which are convenient for parameter estimation. We will mention, in some detail, the various methods of estimation like the various least-squares methods, the maximum likelihood methods, etc., and discuss them regarding their relative accuracy of the estimate and the corresponding computational complexity. We will introduce a new class of estimates, the so-called limited information estimates which utilizes the canonical forms II and III. The accuracy of these estimates is close to that of maximum likelihood, but their computation time is only a fraction of the computation time for the usual maximum likelihood estimates. We will present a few numerical examples to illustrate the various methods. View full abstract»

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  • Stochastic theory of minimal realization

    Publication Year: 1974 , Page(s): 667 - 674
    Cited by:  Papers (44)
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    In this paper it is shown that a natural representation of a state space is given by the predictor space, the linear space spanned by the predictors when the system is driven by a Gaussian white noise input with unit covariance matrix. A minimal realization corresponds to a selection of a basis of this predictor space. Based on this interpretation, a unifying view of hitherto proposed algorithmically defined minimal realizations is developed. A natural minimal partial realization is also obtained with the aid of this interpretation. View full abstract»

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  • Uniqueness of the maximum likelihood estimates of the parameters of an ARMA model

    Publication Year: 1974 , Page(s): 769 - 773
    Cited by:  Papers (63)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (456 KB)  

    Estimation of the parameters in a mixed autoregressive moving average process leads to a nonlinear optimization problem. The negative logarithm of the likelihood function, suitably normalized, converges to a deterministic function as the sample length increases. The local and global extrema of this function are investigated. Conditions for the existence of a unique global and local minimum are given. View full abstract»

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  • Identification and autoregressive spectrum estimation

    Publication Year: 1974 , Page(s): 894 - 898
    Cited by:  Papers (17)
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    In recent years there has been increasing interest in autoregressive spectrum estimation. This procedure fits a finite autoregression to the time series data, and calculates the spectrum from the estimated autoregression coefficients and the one step prediction error variance. For multivariate time series, the estimated autoregressive matrices and one step prediction covariance matrix produce estimates of the spectra, coherences, phases, and group delays. The use of Akaike's information criterion (AIC) for identification of the order of the autoregression to be used makes the procedure objective. Experience gained from analyzing large amounts of data from the biological and physical sciences has indicated that AIC works very well for model identification when compared to more subjective procedures such as the examination of partial F -statistics. This experience has also indicated that using both autoregressive spectrum estimation and classical spectrum estimation and superimposing the plots gives a much stronger feeling for the shape of the true spectrum being estimated. The results of some of these analyses are presented. View full abstract»

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  • Identification and estimation in econometric systems: A survey

    Publication Year: 1974 , Page(s): 855 - 862
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    This is an introductory survey of some of the ideas and methods in the identification and estimation of simultaneous equation systems in econometrics. After pointing out the special features of econometric systems, it defines the problem of identification and presents several methods for estimating the parameters in such systems. Hopefully such a survey will be useful to research workers in related fields including control engineering and statistics who are interested in the estimation of dynamic systems and wish to find out whether the works of econometricians are relevant to their own research. This is not a substitute for a treatise in econometrics, but it may help a researcher in a related field decide whether the techniques developed for dynamic econometric systems are useful for his purpose, whether he should study them in depth, and whether he can contribute to improving them. Some research topics will be suggested later in our discussion. View full abstract»

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  • Some recent advances in time series modeling

    Publication Year: 1974 , Page(s): 723 - 730
    Cited by:  Papers (55)
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    The aim of this paper is to describe some of the important concepts and techniques which seem to help provide a solution of the stationary time series problem (prediction and model identification). Section I reviews models. Section II reviews prediction theory and develops criteria of closeness of a "fitted" model to a "true" model. The central role of the infinite autoregressive transfer function g_{\infty } is developed, and the time series modeling problem is defined to be the estimation of g_{\infty } . Section III reviews estimation theory. Section IV describes autoregressive estimators of g_{\infty } . It introduces a criterion for selecting the Order of an autoregressive estimator which can be regarded as determining the order of an AR scheme when in fact the time series is generated by an AR scheme of finite order. View full abstract»

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  • Canonical matrix fraction and state-space descriptions for deterministic and stochastic linear systems

    Publication Year: 1974 , Page(s): 656 - 667
    Cited by:  Papers (34)
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    Several results exposing the interrelations between state-space and frequency-domain descriptions of multivariable linear systems are presented. Three canonical forms for constant parameter autoregressive-moving average (ARMA) models for input-output relations are described and shown to corrrespond to three particular canonical forms for the state variable realization of the model. Invariant parameters for the partial realization problem are characterized. For stochastic processes, it is shown how to construct an ARMA model, driven by white noise, whose output has a specified covariance. A two-step procedure is given, based on minimal realization and Cholesky-factorization algorithms. Though the goal is an ARMA model, it proves useful to introduce an artificial state model and to employ the recently developed Chandrasekhar-type equations for state estimation. The important case of autoregressive processes is studied and it is shown how the Chandrasekhar-type equations can be used to obtain and generalize the well known Levinson-Wiggins-Robinson (LWR) recursion for estimation of stationary autoregressive processes. View full abstract»

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  • On the structure of a class of nonlinear systems

    Publication Year: 1974 , Page(s): 701 - 706
    Cited by:  Papers (13)
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    Nonlinear systems composed of linear dynamic sub-systems in cascade with static power nonlinearities are considered. A structure theory is developed based on the representation of input/output behavior by rational functions in several variables. An algorithm is given which solves the minimal complete realization problem for two forms of the representation. Examples are worked in detail to illustrate the results. View full abstract»

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  • Channel identification for high speed digital communications

    Publication Year: 1974 , Page(s): 916 - 922
    Cited by:  Papers (14)  |  Patents (1)
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    A channel identification problem arises in high speed digital communication systems that transmit information over time-dispersive channels such as telephone channels and radio channels. This paper deals with algorithms for performing the channel identification rapidly in conjunction with the signal detection to recover the information. The stability and convergence properties of the algorithms are demonstrated. View full abstract»

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  • Strongly consistent parameter estimation by the introduction of strong instrumental variables

    Publication Year: 1974 , Page(s): 825 - 830
    Cited by:  Papers (9)
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    This paper introduces the concept of strong instrumental variables and strong instrumental matrix sequences for the estimation of the transfer function parameters of discrete-time, time-invariant models of linear systems. It is shown that the strong instrumental variable estimators are strongly consistent and a sufficient condition for the estimator to be asymtotically unbiased is given. Moreover, it is shown that with a persistently exciting signal of appropriate order for an input, "virtually" any discrete-time, time-invariant, linear system model of appropriate order can be used to generate strong instrumental variables. View full abstract»

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  • Coupled design of test signals, sampling intervals, and filters for system identification

    Publication Year: 1974 , Page(s): 748 - 752
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (432 KB)  

    This paper discusses the problem of optimal design of experimental conditions for linear system identification. It is demonstrated that, in general, to achieve maximal return from an experiment, coupled design of all the experimental conditions, namely the test signal, sampling intervals and filters, should be carried out simultaneously. For the case of uniform sampling it is shown that joint design of the presampling filter, sampling rate and input can be carded out in the frequency domain. For the case of nonuniform sampling a sequential design procedure is developed which optimizes the information increment between samples. View full abstract»

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  • Gradient estimation algorithms for equation error formulations

    Publication Year: 1974 , Page(s): 820 - 824
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (464 KB)  

    This paper presents the theory for and illustrates the application of gradient parameter estimation algorithms which have been developed for equation error formulations of parameter identification problems. These algorithms are computationally simple; hence, they are ideal candidates for on-line applications. They are limited, however, by availability of first- and second-order statistics of noise processes. View full abstract»

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  • Stable adaptive schemes for state estimation and identification of linear systems

    Publication Year: 1974 , Page(s): 841 - 847
    Cited by:  Papers (24)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (712 KB)  

    The main contribution of this paper is the introduction of a new canonical form for an adaptive observer for a linear multivariable system. The adaptive observer simultaneously estimates the state and the parameters of the unknown plant and is shown to be globally asymptotically stable. The extension of the results to discrete systems and the application of the adaptive observer in the control of helicopter dynamics is also considered in detail. View full abstract»

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Aims & Scope

In the IEEE Transactions on Automatic Control, the IEEE Control Systems Society publishes high-quality papers on the theory, design, and applications of control engineering.  Two types of contributions are regularly considered

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Meet Our Editors

Editor-in-Chief
P. J. Antsaklis
Dept. Electrical Engineering
University of Notre Dame