Ekaterina Gospodinova, Dimitar Nenov, "Mathematical Modeling based on Neural Network Learning for Object Recognition in Automated Systems," WSEAS Transactions on Systems and Control, vol. 19, pp. 427-435, 2024, DOI:10.37394/23203.2024.19.46
Ekaterina Gospodinova, Dimitar Nenov. Mathematical Modeling based on Neural Network Learning for Object Recognition in Automated Systems.
WSEAS Transactions on Systems and Control. 2024;19:427-435. 10.37394/23203.2024.19.46
WSEAS Transactions on Systems and Control
Print ISSN: 1991-8763, E-ISSN: 2224-2856
Volume 19, 2024
Mathematical Modeling based on Neural Network Learning for Object Recognition in Automated Systems
Authors: ,
Abstract: This paper aims to identify efficient methods of mathematically modeling an automated physical system using a neural network. Based on the Levenberg-Marquardt method, we built a feed-forward neural network with the capabilities of a graphics accelerator. The model also sums up and suggests a new neural network training algorithm with Bayes regularization, Nguyen-Widrow initialization, and the early stopping and control method. This greatly expands the efficiency of solving problems where knowledge of an automation system is usable.
Keywords:
mathematical modeling, neural network, automated object, algorithm, Levenberg-Marquardt, CUD)
Pages: 427-435 WSEAS Transactions on Systems and Control, ISSN / E-ISSN: 1991-8763 / 2224-2856, Volume 19, 2024, Art. #46 DOI: 10.37394/23203.2024.19.46
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