Formation of the Convergence Functions of Errors of Input Data of Measurement Systems Computing Components on the Basis of the Finite Automatics Theory

O. Krychevets
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Abstract

This paper presents the results of an investigation into the behavior of the functions of transforming the input data errors for different types of measurement systems’ computing components in order to use their generalized models developed on the basis of the finite automata theory. It is shown that, depending on the kind and value of an input data error transformation function (metrological condition of computing components), the errors of measurement results obtained with the systems’ measuring channels have a determinate character of changes in both static and dynamic regimes of computing components. Determined are the basic dependences of the errors of measurement results upon the input data errors, and upon the types of input data transformation functions; given are the results of their calculation. The investigation results demonstrate a linear character of the dependence of measurement result errors upon the input data errors ΔХ{(tn). In addition, the transformation function calculation f = ΔY{(tn)/ΔХ{(tn) gives its steady state value f = 1,0, i.e. a computing component does not transform the input data error, and does not reverse its sign. For the iterative procedures, the input data errors do not affect the final measurement result, and its accuracy. The measurement error values Δуn depend on the iteration number, and decrease with the increasing number. Of particular interest is the behavior of the function of transforming the input data errors: first, its values are dependent upon the number of iterations; second, f < 1, which clearly shows that the input data errors decrease with the increa­sing number of iterations; and third, the availability of values f = 0 indicates that the function of transforming the input data errors is able to «swallow up» the input data error at the end of the computational procedure. For the linear-chain structures, data have been obtained for a predominantly linear dependence of the measurement error Δs on the input data error Δх, and for the absence of the chain’s transformation function f dependence on the input data errors Δх. For the computing components having a cyclic structure, typi­cal is the same dependence of measurement errors Δt on the input data errors and on the behavior of transformation function ft/x which are specific to the above mentioned computing components that rea­lize iterative procedures. The difference is that the computing components having a cyclic structure realize the so-called (sub)space iteration as opposed to the time iteration specific to the computing components considered. The computing components having a complicated structure (e.g. serial-cyclic, serial-parallel, etc.) demonstrate the dependence of measurement errors on the input data errors which is specific to the linear link that, with such a structure, is determinative for eva­luating the measurement error. Also the function of transforming the input data errors behaves similarly.
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基于有限自动机理论的测量系统计算元件输入数据误差收敛函数的形成
本文介绍了对不同类型测量系统计算组件的输入数据误差转换函数的行为的研究结果,以便使用基于有限自动机理论开发的广义模型。结果表明,根据输入数据误差变换函数的类型和值(计算部件的计量条件),利用系统测量通道获得的测量结果的误差具有计算部件静态和动态变化的确定特征。确定了测量结果的误差对输入数据误差以及输入数据转换函数类型的基本依赖性;给出了它们的计算结果。研究结果表明,测量结果误差与输入数据误差Δx_{(tn)呈线性关系。此外,变换函数计算f=ΔY{(tn)/Δx_{(tn)给出了其稳态值f=1.0,即计算部件不变换输入数据误差,也不反转其符号。对于迭代过程,输入数据的误差不会影响最终的测量结果及其精度。测量误差值Δуn取决于迭代次数,并随着迭代次数的增加而减小。特别令人感兴趣的是转换输入数据误差的函数的行为:首先,它的值取决于迭代次数;其次,f<1,这清楚地表明输入数据误差随着迭代次数的增加而减少;第三,值f=0的可用性表明转换输入数据误差的函数能够在计算过程结束时“吞噬”输入数据误差。对于线性链结构,已经获得了测量误差Δs与输入数据误差Δх的主要线性相关性的数据,以及没有链的变换函数f与输入数据错误Δх相关性的数据。对于具有循环结构的计算组件,测量误差Δt与输入数据误差和变换函数ft/x的行为的相关性通常相同,这是上述实现迭代过程的计算组件特有的。不同之处在于,具有循环结构的计算组件实现了所谓的(子)空间迭代,而不是所考虑的计算组件特有的时间迭代。具有复杂结构(例如串行-循环、串行-并行等)的计算组件证明了测量误差对输入数据误差的依赖性,而输入数据误差是线性链路特有的,具有这种结构的线性链路对评估测量误差是决定性的。此外,转换输入数据误差的功能表现类似。
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