文章基本信息

标题：Convolutional Neural Networks Training for Autonomous Robotics
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作者：Alexander Lozhkin ; Alexander Lozhkin ; Konstantin Maiorov 等
期刊名称：Management Systems in Production Engineering
电子版ISSN：2450-5781
出版年度：2021
卷号：29
期号：1
页码：75-79
DOI：10.2478/mspe-2021-0010
语种：English
出版社：De Gruyter Open
摘要：The article discusses methods for accelerating the operation of convolutional neural networks for autonomous robotics learning. The analysis of the theoretical possibility of modifying the neural network learning mechanism is carried out. Classic semiotic analysis and the theory of neural networks is proposed to union. An assumption is made about the possibility of using the symmetry mechanism to accelerate the training of convolutional neural networks. A multilayer neural network to represent how space is an attempt has been made. The conclusion was based on the laws on the plane obtained earlier. The derivation of formulas turned out to be impossible due to the problems of modern mathematics. A new approach is proposed, which involves combining the gradient descent algorithm and the stochastic completion of convolutional filters by the principles of symmetries. The identified algorithms allow increasing the learning rate from 5% to 15%, depending on the problem that the neural network solves.
关键词：autonomous robots;convolutional neural networks;learning;autoromorphisms;symmetry mechanism;semiotic analyze