Abstract:
The model of a neural network based on metric methods of recognition represents the architecture of a neural network implementing metric methods of recognition. In such networks, the number of neurons, layers and connections, as well as the values of weights can be defined analytically using the initial conditions of a problem (the number of images, templates and attributes). The feasibility of defining network parameters and architecture allows rapid implementation of a network in the case of multitasking application. Finally, we consider multitasking application of neural networks based on metric methods of recognition under different conditions with separately computed classifiers.
This publication is cited in the following 4 articles:
P. Sh. Geidarov, “Experiment for creating a neural network with weights determined by the potential of a simulated electrostatic field”, 49, no. 6, 2022, 519–531
P. Sh. Geidarov, “On the possibility of determining values of the neural network weights by an electrostatic field”, 49, no. 6, 2022, 506–518
P. Sh. Geidarov, “Comparative analysis of the results of training a neural network with calculated weights and with random generation of the weights”, Autom. Remote Control, 81:7 (2020), 1211–1229
P. Sh. Geidarov, “Arkhitektura neironnoi seti s poparno posledovatelnym razdeleniem obrazov”, PDM, 2018, no. 41, 98–109