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ОСУЩЕСТВЛЕНИЕ МЕТРОЛОГИЧЕСКОГО АНАЛИЗА В ПАКЕТЕ
MATLAB
Шпильман Александр Владимирович
Студент
Санкт-Петербургский государственный электротехнический университет, Санкт-Петербург Аннотация . Строятся реализация и гистограммы с помощью генераторов случайных чисел (гсч) в Matlab при равномерном, нормальном, треугольном распределениях.
Ключевые слова: генератор, моделирование, распределение.
Implementation of metrological analysis in the Matlab package
Shpilman Alexandr Vladimirovich
Student
Saint Petersburg State Electrotechnical University, St. Petersburg
Abstract. The implementation and histograms are constructed using random number generators (rng) in Matlab with uniform, normal, triangular distributions.
Key words: generator, modeling, distribution.
Relevance of the topic.
At present, the procedure for metrological analysis using simulation modeling is the most common method for estimating the characteristics of errors in measurement results in information and measurement systems (MIS). In connection with the development of computerization and the complication of scientific experiments, simulation modeling is an integral part of met-rological support for modern measurements. With the
help of simulation modeling, the properties of procedures and measurement results are studied. Metrological analysis with the help of simulation is performed in those cases when a metrological experiment is impossible, and for calculating the estimation of error characteristics it is impossible to form the necessary relationships because of their complexity. To conduct a metro-logical analysis on the basis of simulation, it is necessary to reproduce the input measurement effects.
At the moment, for a metrological analysis using simulation or, in another way, a computer experiment, there is not a single approach to reproducing input measurement effects with known characteristics. Therefore, the topic of the thesis devoted to simulation simulation of input measuring influences with known characteristics is actual. A little studied area of metrological analysis using simulation modeling is the formation of input measurement effects in the form of numerical sequences
Introduction
To extract a class of modeling modifications and concepts, you must first set up the modification view. Modification means a device (and in a large sense any shape), a re-creating, modeling device and the operation of a device. The model is constantly considered a simplification of the present subject, therefore, in no way will it be able to replace it entirely. This in no way less, this fact does not reduce the significance of modifications and forecasting. The model aspires, as it is possible most clearly to formulate the texture of the action, its leaders other nuances. The form is considered an accented formulation of the essence of the object or course, emphasizing only its main features. Knowledge is the data of modifications around society, fixed by the people in his brain or in industrial media. Modifications are overestimated, emphasizing the basic nuances of the essence, and are rapidly used in the actions of comprehension and learning. In the British syllable, two different phrases are used to indicate the progress of modeling: modeling and simulate. The presence of this first text corresponds to the course of design, the formation of the model of the device or the real sphere. A simulation (imitation) takes a study (check, run) of the modification. The simulation is not feasible in the absence
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of an early modification. In its turn, further imitation applies methods of displaying the modification. Proceeding from the more claimed, modeling modifications can be regarded as the only one with modification methods, which implies the subsequent study. A distinctive feature of non-imitation modifications is their immobility, therefore it is possible to classify them as styles of external knowledge mapping or a category of others.
Histograms and implementation according to the laws of probability distribution.
In the statistics, a histogram is a geometric representation of the experimental function of the probability frequency of a certain random variable, constructed in accordance with the sample. The histogram is created in the following way. At first, a huge number of values, which the component of the sample can perform, is broken down into a number of intervals. Most often these intervals are similar, but this is by no means a demanding condition. These intervals are imprinted in the horizontal axis, then a square is displayed above each. In the event that, without exception, all intervals existed similar, then the level of any rectangle is commensurate with the number of sampling components entering the corresponding range. Consider the histograms for different probability distribution laws in the Matlab package. In this case, they were built on a uniform, normal, triangular laws.
Uniform law of probability distribution
The realization for the uniform distribution law takes the form:
Figure - 1 implementation for the uniform law of probability distribution Histogram for the uniform distribution law:
3 Figure 1 - □ X
File Edit View Insert Tools Desktop Window Help
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Гистограмма при равномерн. законе распределения
140 I-1-1-1-1-1-1-1-1-1-
Figure - 2 histograms for the normal probability distribution law
The normal law of probability distribution
Realization for the normal distribution law
Figure 1 - □ X
File Edit View Insert Tools Desktop Window Help *
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Реализация при нормальном законе распрделения
3 2 1
f 0 -1 -2 -3 -4
О 100 200 300 400 500 600 700 800 900 1000 X
Figure - 3 implementation for the normal probability distribution law Histogram for the normal distribution law:
Figure 4 is a histogram for the normal probability distribution law
Triangular law of probability distribution
The realization for the triangular distribution law
Figure 5 implementation for the triangular distribution law
Histogram for the triangular distribution law:
Figure 1
File Edit View [nsert Jools Desktop Window Help
Гистограмма для треуг. закона распр.
m
О 02 04 06 0 3 1 12 1.4 16 18 2
Figure - 6 histogram for the triangular distribution law
Conclusion
For the metrological analysis of the characteristics of errors in measurement results in information and measurement systems, it is convenient to use the simu-
lation method. The multi-window interface, the prevailing command mode for specifying the instructions for generating and processing data in the Matlab package, allows you to perform the reproduction of numerical sequences and thereby perform metrological analysis.
