This chapter provides an overview of interesting phenomena pertaining to the learning capabilities of stochastic-gradient adaptive filters, and in particular those of the least-mean-squares (LMS) algorithm. The phenomena indicate that the learning behavior of adaptive filters is more sophisticated, and also more favorable, than was previously thought, especially for larger step-sizes. The discu...

A new adaptive lter algorithm has been developed that combines the beneets of the Least Mean Square (LMS) and Least Mean Fourth (LMF) methods. This algorithm , called LMS/F, outperforms the standard LMS algorithm judging either constant convergence rate or constant misadjustment. While LMF outperforms LMS for certain noise prooles, its stability cannot be guaranteed for known input signals even...

The least mean squares (LMS) algorithm is one of the most popular recursive parameter estimation methods. In its standard form it does not take into account any special characteristics that the parameterized model may have. Assuming that such model is sparse in some domain (for example, it has sparse impulse or frequency response), we aim at developing such LMS algorithms that can adapt to the ...

This paper presents a discrete time adaptive inversion scheme for linear systems and its usage in adaptive feedforward and feedback controllers. Two parameter adaptation algorithms (PAA) were used in the proposed schemes. The first PAA applies the extended bias-eliminating least-squares (EBELS) algorithm for plant estimation to ensure convergent to a tuned model under colored noise and unmodele...

The Fractional Fourier Transform (FrFT) has wide applications in communications and signal processing. It has been shown to provide significant improvement over the conventional Fourier Transform when the signal-of-interest (SOI) or the interference and noise environment is nonstationary, as is often the case. Recently, a Least-Mean Squares (LMS) algorithm was developed for computing the optimu...

In this paper a new algorithm for multi-channel adaptive least squares adaptive filtering, using a lattice filter is proposed. The algorithm is a multi-channel numerically stable version of the adaptive least squares lattice with a priori errors and error feedback. It is adapted to active noise control using the modified filtered-Error algorithm. It is shown throw computer simulations that the ...

Rapid growth in production of automobiles has increased emissions. Automotive control engineers use innovative control techniques to meet the upcoming emission standards. This paper proposes a novel method of employing artificial neural network (ANN) based predictive controller design. The controller predicts the injection duration based on the inputs from various sensors. The results are then ...

The least-mean-squares (LMS) algorithm is analysed as a feedback control system. It is shown that despite the fact that LMS is a time-variant system that in fact it behaves much as a linear time-invariant (LTI) closed-loop control system. Therefore, it is possible to treat the LMS algorithm as a control system in the classical sense and define properties such as bandwidth to determine how fast ...

This paper describes a method for implementing immersive audio rendering filters for single or multiple listeners and loudspeakers. In particular, the paper is focused on the case of single or two listeners with different loudspeaker arrays to determine the weighting vectors for the necessary FIR and IIR filters using the LMS (least-mean-squares) adaptive inverse algorithm. It describes transfo...