244 research outputs found
Asymptotically Efficient Quasi-Newton Type Identification with Quantized Observations Under Bounded Persistent Excitations
This paper is concerned with the optimal identification problem of dynamical
systems in which only quantized output observations are available under the
assumption of fixed thresholds and bounded persistent excitations. Based on a
time-varying projection, a weighted Quasi-Newton type projection (WQNP)
algorithm is proposed. With some mild conditions on the weight coefficients,
the algorithm is proved to be mean square and almost surely convergent, and the
convergence rate can be the reciprocal of the number of observations, which is
the same order as the optimal estimate under accurate measurements.
Furthermore, inspired by the structure of the Cramer-Rao lower bound, an
information-based identification (IBID) algorithm is constructed with adaptive
design about weight coefficients of the WQNP algorithm, where the weight
coefficients are related to the parameter estimates which leads to the
essential difficulty of algorithm analysis. Beyond the convergence properties,
this paper demonstrates that the IBID algorithm tends asymptotically to the
Cramer-Rao lower bound, and hence is asymptotically efficient. Numerical
examples are simulated to show the effectiveness of the information-based
identification algorithm.Comment: 16 pages, 3 figures, submitted to Automatic
A new kernel-based approach to system identification with quantized output data
In this paper we introduce a novel method for linear system identification
with quantized output data. We model the impulse response as a zero-mean
Gaussian process whose covariance (kernel) is given by the recently proposed
stable spline kernel, which encodes information on regularity and exponential
stability. This serves as a starting point to cast our system identification
problem into a Bayesian framework. We employ Markov Chain Monte Carlo methods
to provide an estimate of the system. In particular, we design two methods
based on the so-called Gibbs sampler that allow also to estimate the kernel
hyperparameters by marginal likelihood maximization via the
expectation-maximization method. Numerical simulations show the effectiveness
of the proposed scheme, as compared to the state-of-the-art kernel-based
methods when these are employed in system identification with quantized data.Comment: 10 pages, 4 figure
Fixed-order FIR approximation of linear systems from quantized input and output data
Abstract The problem of identifying a fixed-order FIR approximation of linear systems with unknown structure, assuming that both input and output measurements are subjected to quantization, is dealt with in this paper. A fixed-order FIR model providing the best approximation of the input-output relationship is sought by minimizing the worst-case distance between the output of the true system and the modeled output, for all possible values of the input and output data consistent with their quantized measurements. The considered problem is firstly formulated in terms of robust optimization. Then, two different algorithms to compute the optimum of the formulated problem by means of linear programming techniques are presented. The effectiveness of the proposed approach is illustrated by means of a simulation example
Large deviations of stochastic systems and applications
This dissertation focuses on large deviations of stochastic systems with applications to optimal control and system identification. It encompasses analysis of two-time-scale Markov processes and system identification with regular and quantized data. First, we develops large deviations principles for systems driven by continuous-time Markov chains with twotime scales and related optimal control problems. A distinct feature of our setup is that the Markov chain under consideration is time dependent or inhomogeneous. The use of two time-scale formulation stems from the effort of reducing computational complexity in a wide variety of applications in control, optimization, and systems theory. Starting with a rapidly fluctuating Markovian system, under irreducibility conditions, both large deviations upper and lower bounds are established first for a fixed terminal time and then for time-varying dynamic systems. Then the results are applied to certain dynamic systems and LQ control problems.
Second, we study large deviations for identifications systems. Traditional system identification concentrates on convergence and convergence rates of estimates in mean squares, in distribution, or in a strong sense. For system diagnosis and complexity analysis, however, it is essential to understand the probabilities of identification errors over a finite data window. This paper investigates identification errors in a large deviations framework. By considering both space complexity in terms of quantization levels and time complexity with respect to data window sizes, this study provides a new perspective to understand the fundamental relationship between probabilistic errors and resources that represent data sizes in computer algorithms, sample sizes in statistical analysis, channel bandwidths in communications, etc.
This relationship is derived by establishing the large deviations principle for quantized identification that links binary-valued data at one end and regular sensors at the other. Under some mild conditions, we obtain large deviations upper and lower bounds. Our results accommodate
independent and identically distributed noise sequences, as well as more general classes of mixing-type noise sequences. Numerical examples are provided to illustrate the theoretical results
Kernel-based identification using Lebesgue-sampled data
Sampling in control applications is increasingly done non-equidistantly in
time. This includes applications in motion control, networked control,
resource-aware control, and event-based control. Some of these applications,
like the ones where displacement is tracked using incremental encoders, are
driven by signals that are only measured when their values cross fixed
thresholds in the amplitude domain. This paper introduces a non-parametric
estimator of the impulse response and transfer function of continuous-time
systems based on such amplitude-equidistant sampling strategy, known as
Lebesgue sampling. To this end, kernel methods are developed to formulate an
algorithm that adequately takes into account the bounded output uncertainty
between the event timestamps, which ultimately leads to more accurate models
and more efficient output sampling compared to the equidistantly-sampled
kernel-based approach. The efficacy of our proposed method is demonstrated
through a mass-spring damper example with encoder measurements and extensive
Monte Carlo simulation studies on system benchmarks.Comment: 16 pages, 8 figure
Sigma-Delta modulation based distributed detection in wireless sensor networks
We present a new scheme of distributed detection in sensor networks using Sigma-Delta modulation. In the existing works local sensor nodes either quantize the observation or directly scale the analog observation and then transmit the processed information independently over wireless channels to a fusion center. In this thesis we exploit the advantages of integrating modulation as a local processor into sensor design and propose a novel mixing topology of parallel and serial configurations for distributed detection system, enabling each sensor to transmit binary information to the fusion center, while preserving the analog information through collaborative processing. We develop suboptimal fusion algorithms for the proposed system and provide both theoretical analysis and various simulation results to demonstrate the superiority of our proposed scheme in both AWGN and fading channels in terms of the resulting detection error probability by comparison with the existing approaches
ΠΠ΅ΡΠΈΠΎΠ΄ΠΎΠ³ΡΠ°ΠΌΠΌΠ½Π°Ρ ΠΎΡΠ΅Π½ΠΊΠ° ΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΠΈ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π±ΠΈΠ½Π°ΡΠ½ΠΎ-Π·Π½Π°ΠΊΠΎΠ²ΠΎΠ³ΠΎ ΡΡΠΎΡ Π°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΎΠΊΠΎΠ½Π½ΡΡ ΡΡΠ½ΠΊΡΠΈΠΉ
Π‘ΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΠΊΠ°ΠΊ ΠΎΠ΄ΠΈΠ½ ΠΈΠ· ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌ ΠΈ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΡΠ°Π·Π»ΠΈΡΠ½ΠΎΠΉ ΡΠΈΠ·ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΡΠΈΡΠΎΠ΄Ρ. Π ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π½Π΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡΠΈ ΡΠΈΠ³Π½Π°Π»Ρ ΠΏΠΎΠ΄Π²Π΅ΡΠ³Π°ΡΡΡΡ ΡΠ»ΡΡΠ°ΠΉΠ½ΡΠΌ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡΠΌ ΠΈ Π·Π°ΡΡΠΌΠ»Π΅Π½ΠΈΡΠΌ. ΠΠ½Π°Π»ΠΈΠ· ΡΠ°ΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡ ΠΊ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π½ΠΈΡ ΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΠΈ ΠΌΠΎΡΠ½ΠΎΡΡΠΈΒ (Π‘ΠΠ). ΠΠ° ΠΏΡΠ°ΠΊΡΠΈΠΊΠ΅ Π΄Π»Ρ Π΅Ρ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π½ΠΈΡ ΡΠΈΡΠΎΠΊΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄. ΠΡΠ½ΠΎΠ²Ρ ΡΠΈΡΡΠΎΠ²ΡΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ², ΡΠ΅Π°Π»ΠΈΠ·ΡΡΡΠΈΡ
ΡΡΠΎΡ ΠΌΠ΅ΡΠΎΠ΄, ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠ΅ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ Π€ΡΡΡΠ΅. Π ΡΡΠΈΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°Ρ
ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ ΡΠΈΡΡΠΎΠ²ΠΎΠ³ΠΎ ΡΠΌΠ½ΠΎΠΆΠ΅Π½ΠΈΡ ΡΠ²Π»ΡΡΡΡΡ ΠΌΠ°ΡΡΠΎΠ²ΡΠΌΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΡΠΌΠΈ. ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΎΠΊΠΎΠ½Π½ΡΡ
ΡΡΠ½ΠΊΡΠΈΠΉ Π²Π΅Π΄Π΅Ρ ΠΊ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΡ ΡΠΈΡΠ»Π° ΡΡΠΈΡ
ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ. ΠΠΏΠ΅ΡΠ°ΡΠΈΠΈ ΡΠΌΠ½ΠΎΠΆΠ΅Π½ΠΈΡ ΠΎΡΠ½ΠΎΡΡΡΡΡ ΠΊ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΡΡΠ΄ΠΎΠ΅ΠΌΠΊΠΈΠΌ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΡΠΌ. ΠΠ½ΠΈ ΡΠ²Π»ΡΡΡΡΡ Π΄ΠΎΠΌΠΈΠ½ΠΈΡΡΡΡΠΈΠΌ ΡΠ°ΠΊΡΠΎΡΠΎΠΌ ΠΏΡΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΡ
Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΡΡ Π΅Π³ΠΎ ΠΌΡΠ»ΡΡΠΈΠΏΠ»ΠΈΠΊΠ°ΡΠΈΠ²Π½ΡΡ ΡΠ»ΠΎΠΆΠ½ΠΎΡΡΡ.
Π ΡΡΠ°ΡΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ Π·Π°Π΄Π°ΡΠ° ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΡ ΠΌΡΠ»ΡΡΠΈΠΏΠ»ΠΈΠΊΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΡΠ»ΠΎΠΆΠ½ΠΎΡΡΠΈ Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΡ ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ Π‘ΠΠ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΎΠΊΠΎΠ½Π½ΡΡ
ΡΡΠ½ΠΊΡΠΈΠΉ. ΠΠ°Π΄Π°ΡΠ° ΡΠ΅ΡΠ°Π΅ΡΡΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π±ΠΈΠ½Π°ΡΠ½ΠΎ-Π·Π½Π°ΠΊΠΎΠ²ΠΎΠ³ΠΎ ΡΡΠΎΡ
Π°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π»Ρ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠ³Π½Π°Π»Π° Π² ΡΠΈΡΡΠΎΠ²ΡΡ ΡΠΎΡΠΌΡ. Π’Π°ΠΊΠΎΠ΅ Π΄Π²ΡΡ
ΡΡΠΎΠ²Π½Π΅Π²ΠΎΠ΅ ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ Π±Π΅Π· ΡΠΈΡΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΠΎΠ³ΡΠ΅ΡΠ½ΠΎΡΡΠΈ. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ΅ΠΎΡΠΈΠΈ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎ-ΡΠΎΠ±ΡΡΠΈΠΉΠ½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΡΠ΅Π·ΡΠ»ΡΡΠ°Ρ Π±ΠΈΠ½Π°ΡΠ½ΠΎ-Π·Π½Π°ΠΊΠΎΠ²ΠΎΠ³ΠΎ ΡΡΠΎΡ
Π°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ Π²ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ ΠΊΠ°ΠΊ Ρ
ΡΠΎΠ½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΡ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
ΡΠΎΠ±ΡΡΠΈΠΉ, ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅ΠΌΡΡ
ΡΠΌΠ΅Π½ΠΎΠΉ Π΅Π³ΠΎ Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎ-ΡΠΎΠ±ΡΡΠΈΠΉΠ½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π΄Π»Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ° Π±ΠΈΠ½Π°ΡΠ½ΠΎ-Π·Π½Π°ΠΊΠΎΠ²ΠΎΠ³ΠΎ ΡΡΠΎΡ
Π°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ»ΠΎ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΠ΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ ΠΈΠ½ΡΠ΅Π³ΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Π΅ ΠΎΡ Π°Π½Π°Π»ΠΎΠ³ΠΎΠ²ΠΎΠΉ ΡΠΎΡΠΌΡ ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ Π‘ΠΠ ΠΊ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ°ΠΌ Π΅Π΅ Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΡ Π² Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠΌ Π²ΠΈΠ΄Π΅. ΠΡΠΈ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ ΡΡΠ°Π»ΠΈ ΠΎΡΠ½ΠΎΠ²ΠΎΠΉ Π΄Π»Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠΈΡΡΠΎΠ²ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°. ΠΡΠ½ΠΎΠ²Π½ΡΠΌΠΈ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠΌΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΡΠΌΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΡΠ²Π»ΡΡΡΡΡ Π°ΡΠΈΡΠΌΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ ΡΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΈ Π²ΡΡΠΈΡΠ°Π½ΠΈΡ. Π£ΠΌΠ΅Π½ΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ ΡΠΌΠ½ΠΎΠΆΠ΅Π½ΠΈΡ ΡΠ½ΠΈΠΆΠ°Π΅Ρ ΠΎΠ±ΡΡΡ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΡ ΡΡΡΠ΄ΠΎΠ΅ΠΌΠΊΠΎΡΡΡ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π½ΠΈΡ Π‘ΠΠ. Π‘ ΡΠ΅Π»ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ°Π±ΠΎΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π±ΡΠ»ΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Ρ ΡΠΈΡΠ»Π΅Π½Π½ΡΠ΅ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡ. ΠΠ½ΠΈ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ»ΠΈΡΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΠΌΠΈΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎ-ΡΠΎΠ±ΡΡΠΈΠΉΠ½ΠΎΠΉ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ Π±ΠΈΠ½Π°ΡΠ½ΠΎ-Π·Π½Π°ΠΊΠΎΠ²ΠΎΠ³ΠΎ ΡΡΠΎΡ
Π°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΏΡΠΈΠΌΠ΅ΡΠ° ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΡ ΠΎΡΠ΅Π½ΠΎΠΊ Π‘ΠΠ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΡΡΠ΄Π° Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΈΠ·Π²Π΅ΡΡΠ½ΡΡ
ΠΎΠΊΠΎΠ½Π½ΡΡ
ΡΡΠ½ΠΊΡΠΈΠΉ. ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠ²ΠΈΠ΄Π΅ΡΠ΅Π»ΡΡΡΠ²ΡΡΡ, ΡΡΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Π²ΡΡΠΈΡΠ»ΡΡΡ ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΡΠ΅ ΠΎΡΠ΅Π½ΠΊΠΈ Π‘ΠΠ Ρ Π²ΡΡΠΎΠΊΠΎΠΉ ΡΠΎΡΠ½ΠΎΡΡΡΡ ΠΈ ΡΠ°ΡΡΠΎΡΠ½ΡΠΌ ΡΠ°Π·ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ΠΌ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΏΡΠΈΡΡΡΡΡΠ²ΠΈΡ Π°Π΄Π΄ΠΈΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π±Π΅Π»ΠΎΠ³ΠΎ ΡΡΠΌΠ° ΠΏΡΠΈ Π½ΠΈΠ·ΠΊΠΎΠΌ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΈ ΡΠΈΠ³Π½Π°Π»/ΡΡΠΌ. ΠΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΎΡΡΡΠ΅ΡΡΠ²Π»Π΅Π½Π° Π² Π²ΠΈΠ΄Π΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎ ΡΠ°ΠΌΠΎΡΡΠΎΡΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄ΡΠ»Ρ. ΠΠ°Π½Π½ΡΠΉ ΠΌΠΎΠ΄ΡΠ»Ρ ΠΌΠΎΠΆΠ΅Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡΡΡ ΠΊΠ°ΠΊ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΠΉ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ Π² ΡΠΎΡΡΠ°Π²Π΅ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈ Π·Π½Π°ΡΠΈΠΌΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ Π΄Π»Ρ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ°ΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΡΠΎΡΡΠ°Π²Π° ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ²
ΠΠ΅ΡΠΈΠΎΠ΄ΠΎΠ³ΡΠ°ΠΌΠΌΠ½Π°Ρ ΠΎΡΠ΅Π½ΠΊΠ° ΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΠΈ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π±ΠΈΠ½Π°ΡΠ½ΠΎ-Π·Π½Π°ΠΊΠΎΠ²ΠΎΠ³ΠΎ ΡΡΠΎΡ Π°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠ³Π½Π°Π»ΠΎΠ² Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΎΠΊΠΎΠ½Π½ΡΡ ΡΡΠ½ΠΊΡΠΈΠΉ
Spectral analysis of signals is used as one of the main methods for studying systems and objects of various physical natures. Under conditions of a priori statistical uncertainty, the signals are subject to random changes and noise. Spectral analysis of such signals involves the estimation of the power spectral density (PSD). One of the classical methods for estimating PSD is the periodogram method. The algorithms that implement this method in digital form are based on the discrete Fourier transform. Digital multiplication operations are mass operations in these algorithms. The use of window functions leads to an increase in the number of these operations. Multiplication operations are among the most time consuming operations. They are the dominant factor in determining the computational capabilities of an algorithm and determine its multiplicative complexity.
The paper deals with the problem of reducing the multiplicative complexity of calculating the periodogram estimate of the PSD using window functions. The problem is solved based on the use of binary-sign stochastic quantization for converting a signal into digital form. This two-level signal quantization is carried out without systematic error. Based on the theory of discrete-event modeling, the result of a binary-sign stochastic quantization in time is considered as a chronological sequence of significant events determined by the change in its values. The use of a discrete-event model for the result of binary-sign stochastic quantization provided an analytical calculation of integration operations during the transition from the analog form of the periodogram estimation of the SPM to the mathematical procedures for calculating it in discrete form. These procedures became the basis for the development of a digital algorithm. The main computational operations of the algorithm are addition and subtraction arithmetic operations. Reducing the number of multiplication operations decreases the overall computational complexity of the PSD estimation. Numerical experiments were carried out to study the algorithm operation. They were carried out on the basis of simulation modeling of the discrete-event procedure of binary-sign stochastic quantization. The results of calculating the PSD estimates are presented using a number of the most famous window functions as an example. The results obtained indicate that the use of the developed algorithm allows calculating periodogram estimates of PSD with high accuracy and frequency resolution in the presence of additive white noise at a low signal-to-noise ratio. The practical implementation of the algorithm is carried out in the form of a functionally independent software module. This module can be used as a part of complex metrologically significant software for operational analysis of the frequency composition of complex signals.Π‘ΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΠΊΠ°ΠΊ ΠΎΠ΄ΠΈΠ½ ΠΈΠ· ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌ ΠΈ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΡΠ°Π·Π»ΠΈΡΠ½ΠΎΠΉ ΡΠΈΠ·ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΡΠΈΡΠΎΠ΄Ρ. Π ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π½Π΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡΠΈ ΡΠΈΠ³Π½Π°Π»Ρ ΠΏΠΎΠ΄Π²Π΅ΡΠ³Π°ΡΡΡΡ ΡΠ»ΡΡΠ°ΠΉΠ½ΡΠΌ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡΠΌ ΠΈ Π·Π°ΡΡΠΌΠ»Π΅Π½ΠΈΡΠΌ. ΠΠ½Π°Π»ΠΈΠ· ΡΠ°ΠΊΠΈΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡ ΠΊ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π½ΠΈΡ ΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΏΠ»ΠΎΡΠ½ΠΎΡΡΠΈ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ (Π‘ΠΠ). ΠΠ° ΠΏΡΠ°ΠΊΡΠΈΠΊΠ΅ Π΄Π»Ρ Π΅Ρ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π½ΠΈΡ ΡΠΈΡΠΎΠΊΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄. ΠΡΠ½ΠΎΠ²Ρ ΡΠΈΡΡΠΎΠ²ΡΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ², ΡΠ΅Π°Π»ΠΈΠ·ΡΡΡΠΈΡ
ΡΡΠΎΡ ΠΌΠ΅ΡΠΎΠ΄, ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠ΅ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ Π€ΡΡΡΠ΅. Π ΡΡΠΈΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°Ρ
ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ ΡΠΈΡΡΠΎΠ²ΠΎΠ³ΠΎ ΡΠΌΠ½ΠΎΠΆΠ΅Π½ΠΈΡ ΡΠ²Π»ΡΡΡΡΡ ΠΌΠ°ΡΡΠΎΠ²ΡΠΌΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΡΠΌΠΈ. ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΎΠΊΠΎΠ½Π½ΡΡ
ΡΡΠ½ΠΊΡΠΈΠΉ Π²Π΅Π΄Π΅Ρ ΠΊ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΡ ΡΠΈΡΠ»Π° ΡΡΠΈΡ
ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ. ΠΠΏΠ΅ΡΠ°ΡΠΈΠΈ ΡΠΌΠ½ΠΎΠΆΠ΅Π½ΠΈΡ ΠΎΡΠ½ΠΎΡΡΡΡΡ ΠΊ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΡΡΠ΄ΠΎΠ΅ΠΌΠΊΠΈΠΌ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΡΠΌ. ΠΠ½ΠΈ ΡΠ²Π»ΡΡΡΡΡ Π΄ΠΎΠΌΠΈΠ½ΠΈΡΡΡΡΠΈΠΌ ΡΠ°ΠΊΡΠΎΡΠΎΠΌ ΠΏΡΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΡ
Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΡΡ Π΅Π³ΠΎ ΠΌΡΠ»ΡΡΠΈΠΏΠ»ΠΈΠΊΠ°ΡΠΈΠ²Π½ΡΡ ΡΠ»ΠΎΠΆΠ½ΠΎΡΡΡ.
Π ΡΡΠ°ΡΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ Π·Π°Π΄Π°ΡΠ° ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΡ ΠΌΡΠ»ΡΡΠΈΠΏΠ»ΠΈΠΊΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΡΠ»ΠΎΠΆΠ½ΠΎΡΡΠΈ Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΡ ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ Π‘ΠΠ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΎΠΊΠΎΠ½Π½ΡΡ
ΡΡΠ½ΠΊΡΠΈΠΉ. ΠΠ°Π΄Π°ΡΠ° ΡΠ΅ΡΠ°Π΅ΡΡΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π±ΠΈΠ½Π°ΡΠ½ΠΎ-Π·Π½Π°ΠΊΠΎΠ²ΠΎΠ³ΠΎ ΡΡΠΎΡ
Π°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π»Ρ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠ³Π½Π°Π»Π° Π² ΡΠΈΡΡΠΎΠ²ΡΡ ΡΠΎΡΠΌΡ. Π’Π°ΠΊΠΎΠ΅ Π΄Π²ΡΡ
ΡΡΠΎΠ²Π½Π΅Π²ΠΎΠ΅ ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠΈΠ³Π½Π°Π»ΠΎΠ² ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ Π±Π΅Π· ΡΠΈΡΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΠΎΠ³ΡΠ΅ΡΠ½ΠΎΡΡΠΈ. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ΅ΠΎΡΠΈΠΈ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎ-ΡΠΎΠ±ΡΡΠΈΠΉΠ½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΡΠ΅Π·ΡΠ»ΡΡΠ°Ρ Π±ΠΈΠ½Π°ΡΠ½ΠΎ-Π·Π½Π°ΠΊΠΎΠ²ΠΎΠ³ΠΎ ΡΡΠΎΡ
Π°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ Π²ΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ ΠΊΠ°ΠΊ Ρ
ΡΠΎΠ½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΡ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
ΡΠΎΠ±ΡΡΠΈΠΉ, ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅ΠΌΡΡ
ΡΠΌΠ΅Π½ΠΎΠΉ Π΅Π³ΠΎ Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎ-ΡΠΎΠ±ΡΡΠΈΠΉΠ½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π΄Π»Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ° Π±ΠΈΠ½Π°ΡΠ½ΠΎ-Π·Π½Π°ΠΊΠΎΠ²ΠΎΠ³ΠΎ ΡΡΠΎΡ
Π°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ»ΠΎ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΠ΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ ΠΈΠ½ΡΠ΅Π³ΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Π΅ ΠΎΡ Π°Π½Π°Π»ΠΎΠ³ΠΎΠ²ΠΎΠΉ ΡΠΎΡΠΌΡ ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ Π‘ΠΠ ΠΊ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ°ΠΌ Π΅Π΅ Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΡ Π² Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠΌ Π²ΠΈΠ΄Π΅. ΠΡΠΈ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ ΡΡΠ°Π»ΠΈ ΠΎΡΠ½ΠΎΠ²ΠΎΠΉ Π΄Π»Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠΈΡΡΠΎΠ²ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°. ΠΡΠ½ΠΎΠ²Π½ΡΠΌΠΈ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠΌΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΡΠΌΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΡΠ²Π»ΡΡΡΡΡ Π°ΡΠΈΡΠΌΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ ΡΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΈ Π²ΡΡΠΈΡΠ°Π½ΠΈΡ. Π£ΠΌΠ΅Π½ΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ ΡΠΌΠ½ΠΎΠΆΠ΅Π½ΠΈΡ ΡΠ½ΠΈΠΆΠ°Π΅Ρ ΠΎΠ±ΡΡΡ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΡ ΡΡΡΠ΄ΠΎΠ΅ΠΌΠΊΠΎΡΡΡ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π½ΠΈΡ Π‘ΠΠ. Π‘ ΡΠ΅Π»ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ°Π±ΠΎΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π±ΡΠ»ΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Ρ ΡΠΈΡΠ»Π΅Π½Π½ΡΠ΅ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡ. ΠΠ½ΠΈ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ»ΠΈΡΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΠΌΠΈΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎ-ΡΠΎΠ±ΡΡΠΈΠΉΠ½ΠΎΠΉ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ Π±ΠΈΠ½Π°ΡΠ½ΠΎ-Π·Π½Π°ΠΊΠΎΠ²ΠΎΠ³ΠΎ ΡΡΠΎΡ
Π°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ²Π°Π½ΡΠΎΠ²Π°Π½ΠΈΡ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΏΡΠΈΠΌΠ΅ΡΠ° ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Π²ΡΡΠΈΡΠ»Π΅Π½ΠΈΡ ΠΎΡΠ΅Π½ΠΎΠΊ Π‘ΠΠ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΡΡΠ΄Π° Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΈΠ·Π²Π΅ΡΡΠ½ΡΡ
ΠΎΠΊΠΎΠ½Π½ΡΡ
ΡΡΠ½ΠΊΡΠΈΠΉ. ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠ²ΠΈΠ΄Π΅ΡΠ΅Π»ΡΡΡΠ²ΡΡΡ, ΡΡΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Π²ΡΡΠΈΡΠ»ΡΡΡ ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΡΠ΅ ΠΎΡΠ΅Π½ΠΊΠΈ Π‘ΠΠ Ρ Π²ΡΡΠΎΠΊΠΎΠΉ ΡΠΎΡΠ½ΠΎΡΡΡΡ ΠΈ ΡΠ°ΡΡΠΎΡΠ½ΡΠΌ ΡΠ°Π·ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ΠΌ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΏΡΠΈΡΡΡΡΡΠ²ΠΈΡ Π°Π΄Π΄ΠΈΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π±Π΅Π»ΠΎΠ³ΠΎ ΡΡΠΌΠ° ΠΏΡΠΈ Π½ΠΈΠ·ΠΊΠΎΠΌ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΈ ΡΠΈΠ³Π½Π°Π»/ΡΡΠΌ. ΠΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΎΡΡΡΠ΅ΡΡΠ²Π»Π΅Π½Π° Π² Π²ΠΈΠ΄Π΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎ ΡΠ°ΠΌΠΎΡΡΠΎΡΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄ΡΠ»Ρ. ΠΠ°Π½Π½ΡΠΉ ΠΌΠΎΠ΄ΡΠ»Ρ ΠΌΠΎΠΆΠ΅Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡΡΡ ΠΊΠ°ΠΊ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΠΉ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ Π² ΡΠΎΡΡΠ°Π²Π΅ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈ Π·Π½Π°ΡΠΈΠΌΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ Π΄Π»Ρ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ°ΡΡΠΎΡΠ½ΠΎΠ³ΠΎ ΡΠΎΡΡΠ°Π²Π° ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ²
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