Implementation of an Approximator Based on a Multilayer Perceptron and Wavelet-Neural Network on the STM32 Microcontroller

Neural network approximators are used in digital signal processing, in particular when automating the process of their analysis. The paper is devoted to the implementation of an approximator based on a multilayer perceptron and a wavelet-neural network. Implementation is carried out on the STM32F401...

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Bibliographic Details
Published in:IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference pp. 1372 - 1377
Main Authors: Bogoslovskii, Ivan A., Stepanov, Andrey B., Ermolenko, Daniil V., Pomogalova, Albina V.
Format: Conference Proceeding
Language:English
Published: IEEE 01.01.2020
Subjects:
approximator
Conferences
Digital signal processing
implementation
microcontroller
Microcontrollers
multilayer perceptron
Multilayer perceptrons
Neurons
STM32
Training
Wavelet analysis
wavelet neural network
ISSN:2376-6565
Online Access:Get full text
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Summary:Neural network approximators are used in digital signal processing, in particular when automating the process of their analysis. The paper is devoted to the implementation of an approximator based on a multilayer perceptron and a wavelet-neural network. Implementation is carried out on the STM32F401RE microcontroller. In the article the speed of training neural networks during the approximation of fragments of biomedical signals is estimated and the influence of the selected activation function of the wavelet-neural network on the approximation error is evaluated. The experimental method shows that for the approximation of electrocardiogram and electroencephalogram when implementing a neural network approximator on the selected microcontroller, the most justified use of the wavelet neural network using the Mexican hat as the activation function of the wavelet is used.
ISSN:2376-6565
DOI:10.1109/EIConRus49466.2020.9039482