Neural network principle of implementation of digital filters

Comparative evaluations of the frequency responses (FR) of two types of filters implemented by the classical and neural network methods are carried out. It is shown that the neural network principle of the implementation of digital filters can serve as an alternative to the classical method for specifically defined parameters of FR in the pass bands and attenuation bands of the frequencies of signal spectrum. The simplest method for calculating the parameters of the filters’ difference equations is the neural network approach, regardless of the type of classification of discrete and digital filters. The implementation of TM (transmultiplexer) on a digital element base requires the use of methods of filtering, modulating and demodulating signals that are largely different from traditional analog methods. The frequency responses of non-recursive types of filters presented in the paper are based on the property of the approximable function determined only in the pass bands and attenuation bands of the frequencies of signal spectrum