DEVELOPMENT OF AN ALGORITHM FOR OPTIMIZING NEURAL NETWORK TRAINING WHEN DETERMINING THE NUMBER OF NEURONS IN A HIDDEN LAYER IN ORDER TO INCREASE THE PROBABILITY OF RECOGNIZING IMAGES OF A GROUND TARGET

Background. High accuracy of recognition of typical ground objects by optoelectronic tracking systems can be achieved by optimizing the parameters of an artificial neural network (INS) such as: the dimension and structure of the INS input signal, synapses of network neurons, the number of neurons of...

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Veröffentlicht in:	Надежность и качество сложных систем H. 4
Hauptverfasser:	Godunov, A.I., Shishkov, S.V., Balanyan, S.T., Al' Saftli, F.Kh
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Penza State University Publishing House 01.02.2022
Schlagworte:	bayesian regularization algorithm goal hidden layer levenberg – marquardt algorithm neural network neural network training optimization probability recognition scalable conjugate gradient algorithm
ISSN:	2307-4205
Online-Zugang:	Volltext
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