The pattern recognition problem solution using a convolutional neural network

Abstract

The article deals with the issues of theoretical and applied modeling based on convolutional neural network in the field of agricultural production. In particular, the specificity of convolutional neural network is considered, its model, architecture and layers are presented. Possibilities of its use for the solution of problems of recognition and classification of images are revealed. In addition, it was found that the convolutional neural network differs from the usual perceptron in that each neural layer is not connected to all the neurons of the previous layer, but only to a part of it. In networks of this type, a small matrix is used for the convolution operation, which moves along the processed layer with a certain offset step. For the first layer, this matrix moves across the input image. After each shift, a signal is generated to activate the neuron of the next layer and from the corresponding position. As a result of studying the problems of image recognition using electronic computers, the possibilities of machine learning as a method of recognizing an object in an image that characterizes each object with a set of features are revealed. In addition, the author reveals the specifics of using the convolutional neural network mathematical apparatus as a model for image recognition, which consists in using an array of data describing the image and applying the method of error back propagation.