RECURRENT NEURAL NETWORK WITH COMBINE TRAINING

Recurrent neural network for identification of dynamic objects is proposed. The network uses weigh sum of neural network output signal and dynamic object output signal (teacher forcing). Such combine training guarantees a reduction of time learning and a safety of identification accuracy. The experimental results of nonlinear dynamic object identification are presented.

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Publication Details

Published
2025-06-15
DOI
https://doi.org/10.21667/978-5-7722-0427-6-160-166
Primary Topic
Neural Networks and Applications
Type
article
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article

RECURRENT NEURAL NETWORK WITH COMBINE TRAINING

В. П. Кузнецов
Neural Networks and Applications
article

RECURRENT NEURAL NETWORK WITH COMBINE TRAINING

В. П. Кузнецов
article en

Abstract

Recurrent neural network for identification of dynamic objects is proposed. The network uses weigh sum of neural network output signal and dynamic object output signal (teacher forcing). Such combine training guarantees a reduction of time learning and a safety of identification accuracy. The experimental results of nonlinear dynamic object identification are presented.

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Neural Networks and Applications
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RECURRENT NEURAL NETWORK WITH COMBINE TRAINING — В. П. Кузнецов · (2025) | TGRS Research Map | TGRS