This study investigates the ability of recursive least squares (RLS) and mean square (LMS) adaptive filtering algorithms to predict quickly track unknown systems. Tracking system behavior is important if there are other parallel systems that must follow exactly same at time. The algorithm can correct filter coefficients according changes in parameters minimize errors between output for input si...

This paper deals with the problem of Adaptive Noise Cancellation (ANC) for the speech signal corrupted with an additive white Gaussian noise. After explaining the least Mean Square (LMS)-based adaptive filter and Kalman filter, it examine the hybrid Kalman-based LMS (KNLMS) technique for adaptation of the ANC. The proposed technique suggests a way to normalize LMS algorithm using Kalman filter....

In hands-free scenarios the desired speech signal picked up by the microphone is corrupted by various disturbances such as additive noise, acoustic echoes, and room reverberation. Especially the cancelation of room reverberation still remains a challenging task. For time-variant acoustic environments adaptive filters with appropriate learning algorithms based on the well-known least-mean-square...

The performance of gradient search adaptive filters, such as the least mean squares (LMS) algorithm, may degrade badly when the filter is subjected to input signals which are corrupted by impulsive interference. The median LMS (MLMS) adaptive filter is designed to alleviate this problem by protecting the filter coefficients from the impact of the impulses. MLMS is a modification of LMS, obtaine...

Device mismatch in VLSI degrades the accuracy of analog arithmetic circuits and lowers the learning performance of large-scale neural networks implemented in this technology. We show compact, low-power on-chip calibration techniques that compensate for device mismatch. Our techniques enable large-scale analog VLSI neural networks with learning performance on the order of 10 bits. We demonstrate...

This paper studies the performance of the Tap Delay line (TDL) and the Gamma Filter in the application of echo cancellation. The TDL and Gamma filter architecture is implemented using the Least Mean Squares (LMS) algorithm. Experimental results indicate that the Gamma filter outperforms the TDL with respect to the intelligibility of the speech recovered and the number of tap weights used in the...

The wireless network highly affected by the interference in the spectrum which reduces the throughput of the network. In order to reduce the problem going for the beamforming technology. In this paper we suggest different training sequence algorithms like Recursive Least Squares (RLS) and Least Mean Squares (LMS) are analysed and compared. The simulation is based on the single user networks whi...

• Students will learn how to develop a simple three layer neural network consisting of: a two node input layer, a one node output layer, and a hidden layer that connects the two and is governed by the least mean squares (LMS) algorithm. From these principles, Matlab’s neural network toolbox will then be used to design meaningful, large-scale neural networks for realistic weather radar data sena...

Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advantages of both least mean square (LMS) and least mean fourth (LMF). The advantage of LMS is fast convergence speed while its shortcoming is suboptimal solution in low signal-to-noise ratio (SNR) environment. On the contrary, the advantage of LMF algorithm is robust in low SNR while its drawback is ...

In the areas of acoustic research or applications that deal with not-precisely-known or variable conditions, a method of adaptation to the uncertainness or changes is usually necessary. When searching for an adaptation algorithm, it is hard to overlook the least mean squares (LMS) algorithm. Its simplicity, speed of computation, and robustness has won it a wide area of applications: from teleco...