Identification of Tones with Noises by Artificial Intelligence

Abstract

The paper presents the results of the application of artificial backpropagation neural networks in identification of signal frequency tones with different RMS noise level. The Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) training algorithms were applied in the processes of neural synthesis. Three-layer with 35 hidden neurons and four-layer architectures with 22 in the first and 11 neurons in the second hidden layer in hyperbolic tangent transfer functions with accuracies 96.00% and 98.00% in LM were selected. For SCG with softmax output activation function a neural network with the best accuracy 94.3% in 29 hidden neurons was synthesized.