In this paper, we propose a computationally efficient Legendre Neural Network (LNN) for nonlinear Active Noise Cancellation (NANC). Update algorithms for NANC with linear secondary path (LSP) based on Filtered-x Least Mean Square (FXLMS), Filtered-e Least Mean Square(FELMS) and Recursive Least Square(RLS) are developed. Update algorithm for NANC with nonlinear secondary path(NSP) is also develo...

4 square-root Schur RLS adaptive filter with two-fold (inputand residual) normalization is presented. The algorithm has severalattractive features suchas a fully systolic structure based on elementaryhyperbolic plane rotations. All internal variables are bounded in theunit interval and fully utilize it in successive stages due to an inherent‘‘ autoscaling ” property of t...

The computational complexity of the multiple-input and multiple-output (MIMO) based least square algorithm is very high it cannot be run on processing-inefficient low-cost platforms. To overcome complexity-related problems, a parallel distributed adaptive signal processing (PDASP) architecture proposed, which framework used to efficiently filtering algorithms having cost. In this paper, communi...

Radio Frequency (RF) interference is inherent in all wireless systems and is one of the most significant design parameters of cellular and other mobile systems. In this paper, it is shown that how a non-linear adaptive Volterra filter (Polynomial filter where input and output signals are related through Volterra series) helps track the statistics of the input data and dynamics of a direct seque...

Wireless communication systems operating over time-varying fading channels require adaptive signal processing to equalize the channel variations at the receiver. In wireless applications, the received signal is typically affected by frequency-selective fading and channel equalization is required to mitigate the resulting inter symbol interference (ISI). In this paper an adaptive model has been ...

A robust recursive least-squares (RLS) adaptive filter against impulsive noise is proposed for the situation of an unknown desired signal. By minimizing a saturable nonlinear constrained unsupervised cost function instead of the conventional least-squares function, a possible impulse-corrupted signal is prevented from entering the filter’s weight updating scheme. Moreover, a multi-step adaptive...

Recently, recursive least square (RLS), or extended Kalman ltering (EKF), based algorithms have been demonstrated to be a class of eeective online training methods for neural networks. This paper discusses several aspects of pruning a neural network trained by the RLS based approach. Based on our study, the RLS approach is implicitly a weight decay training algorithm. Also, we derive two prunin...

The application of the least-mean-square (LMS) and recursive-least-square (RLS) algorithms to the estimation of symbol period is discussed. The algorithms are based on the measurements of time between two consecutive detected transitions in noisy waveforms. Two versions of the algorithm are developed, for white and colored measurement noise model. Conditions are derived that guarantee proper be...

This paper compares performance of finite impulse response (FIR) adaptive linear equalizers based on the recursive least-squares (RLS) and least mean square (LMS) algorithms in nonstationary uncorrelated scattering wireless channels. Simulation results, in terms of steady-state mean-square estimation error (MSE) and average bit-error rate (BER) metrics, are found for the frequency-selective Ray...

Aerodynamic parameter estimation is critical in the aviation sector, especially design and development programs of defense-military aircraft. In this paper, new results application Artificial Neural Networks (ANN) to field aircraft are presented. The performances Feedforward Network (FFNN) with Backpropagation FFNN using Recursive Least Square (RLS) investigated for aerodynamic estimation. meth...