Results 111 to 120 of about 794 (150)

Estimation of the Impulse Response of the AWGN Channel with ISI within an Iterative Equalization and Decoding System That Uses LDPC Codes. [PDF]

Entropy (Basel)
Cuc AM, Morgoș FL, Grava AM, Grava C.
europepmc   +1 more source

Adaptive state-feedback echo state networks for temporal sequence learning. [PDF]

Sci Rep
Lupascu CA, Coca D.
europepmc   +1 more source

Respiratory Monitoring in Motion: An Overview of Wearable Methods and Algorithmic Approaches for Reliable Assessment. [PDF]

Biosensors (Basel)
Pecik M, Vavrinsky E, Vitazkova D, Kosnacova H, Nevrela J, Foltan E.   +5 more
europepmc   +1 more source

Adaptive regulation of a non inverting buck boost converter with Levenberg Marquardt based sliding mode predictive control scheme. [PDF]

Sci Rep
Asvadi-Kermani O, Sorouri H, Oshnoei A.
europepmc   +1 more source

Effluent quality soft sensor for wastewater treatment plant with ensemble sparse learning-based online next generation reservoir computing. [PDF]

Water Res X
Fang G, Huang D, Wu Z, Chen Y, Li Y, Liu Y.   +5 more
europepmc   +1 more source

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The fast subsampled-updating recursive least-squares (FSU RLS) algorithm for adaptive filtering based on displacement structure and the FFT

Signal Processing, 1994
Abstract In this paper, we derive a new fast algorithm for Recursive Least-Squares (RLS) adaptive filtering. This algorithm is especially suited for adapting very long filters such as in the acoustic echo cancelation problem. The starting point is to introduce subsampled updating (SU) in the RLS algorithm. In the SU RLS algorithm, the Kalman gain and
Dirk T. M. Slock, Karim Maouche
openaire   +4 more sources

A Recursive Least-Squares (RLS) Algorithm Based on Interpolation Lattice Recursion

2006 2nd International Conference on Information & Communication Technologies, 2006
This paper proposes using a square-root-free (SRF) QR-decomposition-based least-squares lattice (QRD-LSL) interpolation algorithm to implement the recursive least-squares (RLS) algorithm that requires O(Nlog 2 N) operations. Our simulation results show that the proposed RLS algorithm appears to be numerically stable.
null Jenq-Tay Yuan, null Chih-An Chiang
openaire   +1 more source

Modified recursive least squares (RLS) algorithm for neural networks using piecewise linear function

IEE Proceedings - Circuits, Devices and Systems, 2004
The recursive least squares (RLS) learning algorithm for multilayer feedforward neural networks uses a sigmoid nonlinearity at node outputs. It is shown that by using a piecewise linear function at node outputs, the algorithm becomes faster. The modified algorithm improves computational efficiency and by preserving matrix symmetry it is possible to ...
A.P. Gokhale, P.M. Nawghare
openaire   +1 more source