The ADAptive LINear Element (ADALINE) neural network uses Least Mean Square (LMS) learning rule. This paper presents a comprehensive comparison of three different variable learning rate (VLR) parameter LMS algorithms, for the generalized ADALINE neural network paradigms. These algorithms are used to adjust the weights of the ADALINE neural network, which are tested under three different applica...

in this paper, a neural network model reference adaptive system speed observer is designed, which can be used in speed control of linear induction motors (lims). dynamical equations of lim have been considered accurate. in other words, the end effect and the electrical losses of the motor have been included in the motor equivalent circuit. then equations of the reference model and adaptive mode...

Active power filter (APF) has now become a mature technology for harmonic and reactive power compensations in two-wire (single phase), three-wire (three phase without neutral), and four-wire (three phase with neutral) ac power networks with nonlinear loads. This paper presents a study on three different adaptive algorithms for active power filtering applications. These algorithms are adaptive l...

In this paper we introduce the generalized feedforward filter, a new class of adaptive filters that combine attractive properties of Finite Impulse Response (FIR) filters with some of the power of Infinite Impulse Response (IIR) filters. A particular case, the adaptive gamma filter, generalizes Widrow’s adaptive linear combiner (adaline) to an infinite impulse response filter. Yet, the stabilit...

In this paper, a neural network model reference adaptive system speed observer is designed, which can be used in speed control of linear induction motors (LIMs). Dynamical equations of LIM have been considered accurate. In other words, the end effect and the electrical losses of the motor have been included in the motor equivalent circuit. Then equations of the reference model and adaptive mode...

A new strategy to estimate harmonic distortion from an AC line is presented for power electronic converters. An Adaptive linear neural network (ADALINE) is used to determine precisely the necessary currents in order to cancel harmonics. The proposed strategy is based on an original decomposition of the measured currents to specify the neural network inputs. This new decomposition is based on th...

In this paper, we report some results on hardware and software co-design of an adaptive linear neuron (ADALINE) based control system. A discrete-time Proportional-Integral-Derivative (PID) controller is designed based on the mathematical model of the plant. The parameters of the plant model are identified on-line by an ADALINE neural network. In order to efficiently and economically implement t...

In this paper two new methods has been used for artifact denoising in EEG signals, the first Method is based on Wavelet transform and the second method is based on adaptive linear neural networks (ADALINE), the simulation results are very promising.

This article describes linear and nonlinear Artificial Neural Network(ANN)-based predictors as Autoregressive Moving Average models with Auxiliary input (ARMAX) process for Signal to Interference plus Noise Ratio (SINR) prediction in Direct Sequence Code Division Multiple Access (DS/CDMA) systems. The Multi Layer Perceptron (MLP) neural network with nonlinear function is used as nonlinear neura...

The paper presents a new approach for the estimation of harmonic components of a power system using a linear adaptive neuron called Adaline. The learning parameters in the proposed neural estimation algorithm are adjusted to force the error between the actual and desired outputs to satisfy a stable difference error equation. The estimator tracks the Fourier coefficients of the signal data corru...