Оценка направления прихода сигнала с использованием искусственных нейронных сетей максимального правдоподобия
Feedforward artificial neural network based approach to implementing maximum likelihood direction-of-arrival estimator for passive radar systems is proposed. Comparison of the proposed estimator, optimal numerical solution and referenced Cramer-Rao lower bound was carried out; absence of significant dependency of the estimator accuracy in wide angle range is shown.
Procedure for the simulation of the advances in EGE from mathematics is considered. For some tasks the important predictors are obtained. The models of binary logistics regression and ordinal regression for the prediction of probabilities of solution of task are built.
We demonstrate that classical quadratic forms are not able to solve the problem of recognizing highdimensional images. The "deep" GalushkinHinton neural networks can solve the problem of highdimensional image recognition, but their training has exponential computational complexity. It is technically impossible to train and retrain a "deep" neural network rapidly. For mobile "artificial nose" systems we proposed to employ a number of "wide" neural networks trained in accordance with (GOST R 52633.52011). This standardized learning algorithm has a linear computational complexity, i.e. for each new smell image a time of about 0.3 seconds is sufficient for creating and training a new neural network with 2024 inputs and 256 outputs. This leads to the possibility of the rapid training of the artificial intelligence "artificial nose" and a gradual expansion of its database consisting of 10 000 or more trained artificial neural networks.
In aerospace industry one of the main issues is the problem of the qualified specialists education. During the learning process positive incentives improve the effectiveness of the education . One of such incentives is the rating system. In this work the construction and evaluation of the specialized rating system is regarded with examples on the distance learning system that is used for learning mathematical courses by students of aerospace disciplines.
This proceedings publication is a compilation of selected contributions from the “Third International Conference on the Dynamics of Information Systems” which took place at the University of Florida, Gainesville, February 16–18, 2011. The purpose of this conference was to bring together scientists and engineers from industry, government, and academia in order to exchange new discoveries and results in a broad range of topics relevant to the theory and practice of dynamics of information systems. Dynamics of Information Systems: Mathematical Foundation presents state-of-the art research and is intended for graduate students and researchers interested in some of the most recent discoveries in information theory and dynamical systems. Scientists in other disciplines may also benefit from the applications of new developments to their own area of study.