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In this paper, questions oriented at improving frequency domain representation of a digital signal with a considerably restricted number of samples are discussed. The proposed approach is based on adaptive synthesis implemented by means of a proper evolutionary algorithm. As compared to previously proposed techniques, our method is much more effective. This takes place, for instance, when the analysis...
The issue of improving methodology of creating frequency-domain picture of time-domain signals with a small number of samples is considered. The proposed method is a modification of the one published in [9]. Like in [9], our method is based on applying an evolutionary algorithm to signal analysis in frequency domain. This is unlike in classical methods, where discrete time signals are described using...
In this paper, improvement of learning atypical networks (an increase of precision, speed and reliability), used to determine coefficients of functions describing hidden laws acting in a set of empirical data is presented. The learning concerns networks, which are based on rational functions. Proper expression of the used rational function is required. A new element of our approach to the problem...
In this paper, the problem of efficient learning specific neural networks including reciprocal activation functions of the 1/(.) type is discussed. The considered networks can be used, when applying polynomial descriptions, to create symbolic models of unknown laws governing a given set of empirical data. Coefficients of the polynomials are determined in the process of learning the network. However,...
For a few given RH sensors and modules, various regression methods for obtaining the calibration equations have been used, and the results have been compared to each other as well as to the results obtained using the minimax method of approximation by polynomial. The approximation error of each equation was estimated, and the conclusions concerning methods for determining of calibration equations...
In this paper, a possibility of discovering laws governing empirical data whose interrelations can be expressed in a multidimensional polynomial form is considered. A novel atypical perceptron with reciprocal type activation functions is proposed. This perceptron implements the polynomial relation and enables determining the polynomial coefficients by training the perceptron. The perceptron is simple...
In this paper, a possibility of discovering laws governing empirical data by means of special type neural networks is discussed. We outline main idea and present new networks suitable for this task. The network presentation is combined with a preliminary classification of the applied symbolic relationships used to describe a given numerical data. We also show what operators can play a role of activation...
Podano warunki wystarczające dla istnienia przybliżenia Czebyszewa charakterystyki statycznej sensora wyrażeniem wykładniczym z interpolowaniem w skrajnych punktach przedziału. Opisano algorytm konstruowania ciągłego przybliżenia minimaksowego w postaci funkcji sklejanej wykładniczej z zadaną wartością błędu. Przedstawiono przykład zastosowania takiego przybliżenia funkcją sklejaną do opisu charakterystyki...
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