This work considers a model for measuring non-additive quantities, in particular a model for subjective measurement. The purpose of this work was to develop the measurement theory and form of a measurement model that uses the corrected S. Stevens measurement model.A generalized structure was considered that included an empirical system, a mathematical system, and a homomorphism of the empirical system into a numerical system. The main shortcomings of classical measurement theories seem to be: 1) homomorphism does not display operations (in this case, one cannot speak of the meaningfulness of the model); and 2) there is no empirical measurement model that could confirm the existence of a homomorphism. To overcome the shortcomings of existing theories a definition of the measurement equation is given. As a result a measurement model is obtained that is free from the shortcomings of classical measurement theories. The model uses the corrected model of S. Stevens and the reflection principle of J. Barzilai.The measurement model was tested using laws that were obtained empirically. Using the model it is shown that Fechnerʼs empirical law is equivalent to Stevensʼs empirical law. This means that the problem which has attracted attention of many researchers for almost a century, has been solved.A numerical example demonstrates the possibilities of the proposed measurement model. It is shown that the model can be used for extended analysis of expert assessments.

Physical quantities are distinguished from non-physical quantities the method of measurement. In addition, when measuring physical quantities, the concept of identical objects is considered. For example, is equally likely outcomes in classical probability theory or equality of scale interval of a measuring scale. For nonphysical size we will take measurements by subjective estimation in an order scale, but also to use an undefined notion of the sequence of equally different objects. This approach has been used successfully in some researches for subjective characteristics of the objects. For example, the sequence of stars in the sky of various brightness or levels of difficulty of the test. The numbers of members of such a sequence are called ratings. Having defined rating, it is possible to find values of size if to consider that to equally different objects there corresponds the identical result of paired comparison. As the expert compares objects, without determining the sizes of objects, it is natural to assume that the way of comparison is not known to him. It means that as mathematical model we defined an indirect way of finding of values of nonphysical size at an unknown way of comparison. Having chosen a way of comparison, each object can put number which we will call the subjective size of an object in compliance. In a metrology of ways of numerical comparison of physical quantities only two is a difference and the relation. Therefore at assessment of subjective sizes we will be limited in two ways – a difference and the relation of the sizes of sizes.As an example of application of the theory the functional communication, between physical quantity and nonphysical size, established by empirical laws is analyzed. It is noted that Fekhner and Stephens’s empirical laws use a difference or the relation of subjective sizes. But the difference or the relations of sizes can be expressed through the difference of ratings. Therefore there is an opportunity for each law to receive a ratio between the difference of ratings and physical quantity. Coincidence of two laws of Fekhner and Stephens, after transition to rating, confirms reliability of our model.

The problem of approximation is relevant for most engineering applications. In this connection, the universal methods of approximation are of interest. The method of nonparametric approximation is developing in the paper – the method of singular wavelets. The method includes an effective numerical algorithm based on the summation of a recursive sequence of functions. The universal algorithm of approximation makes it possible to apply it to approximate one-dimensional and multidimensional functions, in decision support systems, in the processing of stochastic information, pattern recognition, and solution of boundary-value problems.The introduction explain the idea of the method of singular wavelets – to combine the theory of wavelets with the Nadaraya-Watson kernel regression estimator. Usually, Nadaraya-Watson kernel regression are considered as an example of non- parametric estimation. However, one parameter, the smoothing parameter, is still present in the traditional kernel regression algorithm. The choice of the optimal value of this parameter is a complex mathematical problem, and numerous studies have been devoted to this question. In the approximation by the method of singular wavelets, summation of Nadaraya-Watson kernel regression estimates with the smoothing parameter takes place, which solves the problem of the optimal choice of this parameter.In the main part of the paper theorems are formulated that determine the properties of the regularized wavelet transform. Sufficient conditions for uniform convergence of the wavelet series are obtained for the first time. To illustrate the effectiveness of the numerical approximation algorithm, we consider an example of the quasi-interpolation of the Runge function by wavelets with a uniform distribution of interpolation nodes.

In this paper, we propose to use a discrete wavelet transform with a singular wavelet to isolate the periodic component from the signal. Traditionally, it is assumed that the validity condition must be met for a basic wavelet (the average value of the wavelet is zero). For singular wavelets, the validity condition is not met. As a singular wavelet, you can use the Delta-shaped functions, which are involved in the estimates of Parzen-Rosenblatt, Nadaraya-Watson. Using singular value of a wavelet is determined by the discrete wavelet transform. This transformation was studied earlier for the continuous case. Theoretical estimates of the convergence rate of the sum of wavelet transformations were obtained; various variants were proposed and a theoretical justification was given for the use of the singular wavelet method; sufficient conditions for uniform convergence of the sum of wavelet transformations were formulated. It is shown that the wavelet transform can be used to solve the problem of nonparametric approximation of the function. Singular wavelet decomposition is a new method and there are currently no examples of its application to solving applied problems. This paper analyzes the possibilities of the singular wavelet method. It is assumed that in some cases a slow and fast component can be distinguished from the signal, and this hypothesis is confirmed by the numerical solution of the real problem. A similar analysis is performed using a parametric regression equation, which allows you to select the periodic component of the signal. Comparison of the calculation results confirms that nonparametric approximation based on singular wavelets and the application of parametric regression can lead to similar results.

Integral transformations on a finite interval with a singular basis wavelet are considered. Using a sequence of such transformations, the problem of nonparametric approximation of a function is solved. Traditionally, it is assumed that the validity condition must be met for a basic wavelet (the average value of the wavelet must be zero). The paper develops the previously proposed method of singular wavelets when the tolerance condition is not met. In this case Delta-shaped functions that participate in Parzen – Rosenblatt and Nadaray – Watson estimations can be used as a basic wavelet. The set of wavelet transformations for a function defined on a numeric axis, defined locally, and on a finite interval were previously investigated. However, the study of the convergence of the decomposition on a finite interval was carried out only in one particular case. It was due to technical difficulties when trying to solve this problem directly. In the paper the idea of evaluating the periodic continuation of a function defined initially on a finite interval is implemented. It allowed to formulate sufficient convergence conditions for the expansion of the function in a series. An example of approximation of a function defined on a finite interval using the sum of discrete wavelet transformations is given.