Optimization of Neural Network Training for Image Recognition Based on Trigonometric Polynomial Approximation

The paper discusses optimization issues of training Artificial Neural Networks (ANNs) using a nonlinear trigonometric polynomial function. The proposed method presents the mathematical model of an ANN as an information transmission system where effective techniques to restore signals are widely used...

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Veröffentlicht in:	Programming and computer software 2021-12, Vol.47 (8), p.830-838
Hauptverfasser:	Vershkov, N., Babenko, M., Tchernykh, A., Pulido-Gaytan, B., Cortés-Mendoza, J. M., Kuchukov, V., Kuchukova, N.
Format:	Artikel
Sprache:	eng
Schlagworte:	Approximation Artificial Intelligence Artificial neural networks Classification Computer Science Data transmission Expected values Feature selection Fourier transforms Information processing Machine learning Mathematical analysis Mathematical models Neural networks Operating Systems Optimization Polynomials Signal processing Signal to noise ratio Software Engineering Software Engineering/Programming and Operating Systems Spectrum analysis Supervised learning Variables
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