Adaptive Neural Networks for Robust Estimation of parameters of Noisy Harmonic Signals

In many applications, very fast methods are required for estimating and measurement of parameters of harmonic signals distorted by noise. This follows from the fact that signals have often time varying amplitudes. Most of the known digital algorithms are not fully parallel, so that the speed of processing is quite limited. In this paper we propose new parallel algorithms, which can be implemented by analogue adaptive circuits employing some neural network principles. The problem of estimation is formulated as an optimization problem and solved by using the gradient descent method. Algorithms based on the least-squares (LS), the total least-squares (TLS) and the robust TLS criteria are developed and compared. The networks process samples of observed noisy signals and give as a solution the desired parameters of signal components. Extensive computer simulations confirm the validity and performance of the proposed algorithm

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks

Neural networks for real-time estimation of parameters of signals in power systems

Fast determination of parameters of the fundamental waveform of voltages and currents is essential for the control and protection of electrical power systems. Most of the known digital algorithms are not fully parallel, so that the speed of processing is quite limited. New parallel algorithms, which can be implemented by analogue adaptive circuits employing some neural networks principles, are proposed. The problem of estimation is formulated as an optimization problem and solved by using the gradient descent method. Algorithms based on the least absolute value, the minimax, the least-squares and the robust leastsquares criteria are developed and compared. The networks process samples of observed noisy signals (voltages or currents) and give as a solution the desired parameters of signal components. Extensive computer simulations confirm the validity and performance of the proposed algorithms and neural network realizations. The proposed methods seem to be particularly useful for real-time, high-speed estimation of parameters of sinusoidal signals in electrical power systems

Neural networks for real-time estimation of parameters of signals in power systems

The purpose of this paper is to present new algorithms and along with them new architectures of analogue neuron-like adaptive processors for online estimation of parameters of sinusoidal signals, which are distorted by higher harmonics and corrupted by noise. For steady-state conditions we have developed neural networks which enable us to estimate the amplitudes and the frequency of the fundamental component of signals. When estimating the basic waveform of currents during short circuits the exponential DC component distorts the results. Assuming the known frequency, we have developed adaptive neural networks which enable us to estimate the amplitudes of the basic components as well as the amplitudes and the time constant of a DC component. The problem of estimation of signal parameters is formulated as an unconstrained optimization problem and solved by using the gradient descent continuous-time method. Basing on this approach we have developed systems of nonlinear differential equations that can be implemented by analog adaptive neural networks. The solution of the optimization problem bases on some principles given by Tank and Hopfield [ 4 ] as well as by Kennedy and Chua. The developed networks contain elements which are similar to the adaptive threshold elements of the perceptron presented by Widrow