Informatica

This paper contains measures to describe the matrix impulse response sensitivity of state space multivariable systems with respect to parameter perturbations. The parameter sensitivity is defined as an integral measure of the matrix impulse response with respect to the coefficients. A state space approach is used to find a realization of impulse response that minimizes a sensitivity measure.

Statistical properties are examined for a class of pipelined-block linear time-varying (LTV) and linear time-invariant (LTI) discrete-time systems. Pipelined-block equations are derived, using the general solution of LTV discrete-time system in state space. Afterwards, we analysed the state covariance and output covariance matrices of pipelined-block LTV and LTI discrete-time systems in state space. For this class of pipelined-block realizations expressions are found for calculation of characteristics of the roundoff noise. Finally, scaling in the pipelined LTV discrete-time systems in state space is considered.

In this paper, we propose to present the direct form recursive digital filter as a state space filter. Then, we apply a look-ahead technique and derive a pipelined equation for block output computation. In addition, we study the stability and multiplication complexity of the proposed pipelined-block implementation and compare with complexities of other methods. An algorithm is derived for the iterative computation of pipelined-block matrices.

The input–output relationship of the periodically time-varying (PTV) systems, impulse response of the PTV state–space system, and the transfer function of the PTV system are presented. A coefficient sensitivity is investigated by using a virtual PTV state-space system in which periodically time-varying coefficients are stochastically varied.

The present paper considers a constant-parameter estimation algorithm for an analog transfer function by discrete observations of the object's input and output variables. The algorithm is based on supplementary variables and least-squares methods. It is assumed, that the order of the transfer function is known and the derivatives of the input and output variables are non-measurable. The supplementary variables and their derivatives are constructed from discrete observations of the input and output variables by applying a numerically realized analog filter. Investigation results for the estimate properties are presented. The results were obtained by the method of statistical simulation.