СОЗДАНИЕ RLCP-СОВМЕСТИМЫХ ВИРТУАЛЬНЫХ ЛАБОРАТОРИЙ ДЛЯ ОБУЧЕНИЯ БАЗОВЫМ АЛГОРИТМАМ НА НЕЙРОННЫХ СЕТЯХ

Abstract

The article describes the principles of developing RLCP-compatible virtual laboratories. There are build two virtual laboratories based on these principles for mastering the basic algo-rithms on neural networks: Algorithm for Sequential Signal Propagation in Perceptron and Algorithm for Training Perceptron Using Method of Backward Error Propagation. Virtual laboratories consist of two independent modules – a virtual stand and an RLCP server. The virtual stand implements a visual display of the task's data and provides the listener with tools for forming and editing intermediate solutions and responses. Since the virtual laboratories were assumed for the first acquaintance with neural networks, the simplest neural network architectures in the form of single-layer perceptrons were used as the initial data. And the algorithm of sequential propagation of signals in a neural network (VL1) and the algorithm of training a neural network with a teacher based on the method of inverse error propagation (VL2) are used as the basic algorithms. For automatic generation of equally complex and valid tasks there have been proposed algorithms with high efficiency (the average time for generating an individual task on the VL2 stand for a student was no longer than 3 seconds). It was found out experimentally that such virtual laboratories should be created in two modes: the mode of training and mode of certification. The training shop works for solving problems using the studied algorithms on the stands of virtual laboratories in the training mode with the diagnosis of admitted errors significantly increase the effectiveness of students' results