Development of a Cluster with Cloud Computing Based on Neural Networks With Deep Learning for Modeling Multidimensional Fields

Abstract

Introduction. The modeling of multidimensional fields on multiprocessors, with a neural network architecture, which is rebuilt in the process of solving the problem by means of deep learning, is considered. This architecture of the calculator allows the device to be used to solve the problems of passive location, monitoring station, active LPI location station, base telecommunications station at the same time. Particular attention is paid to the use of bionic principles in the processing of multidimensional signals. A cluster computer with cloud computing is proposed for creating a modeling complex for processing multidimensional signals and debugging the target system. The cluster is made in the form of a multiprocessor based on neural network technology with deep learning. Biomimetic principles are used in the architecture of the modeling complex. The purpose of the work. Creation of a modeling complex as a cluster with cloud computing using neural networks with deep learning. The cluster is a neuromultiprocessor that is rebuilt in the process. Results. In the process, we managed to create a multiprocessor, which in the process of computing is rebuilt, to simulate a terahertz 3D Imager scanner using cloud computing. Conclusions. In the process of performing the work a complex for modeling multidimensional signals was created. As the basis of the computer used a cluster that is rebuilt in the process. The computing base consists of neural networks with cloud computing. Keywords: cognitive space, deep learning, convolutional neural network, neural network architectures, cluster.