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Recursive least squares filter

About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.


Papers
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Patent
Kyeong Jin Kim1
16 May 2007
TL;DR: In this paper, an apparatus having a detector for an iterative LDPC-coded MIMO-OFDM system, where the detector is configured to use a structured irregular LDPC code in conjunction with a belief propagation algorithm is presented.
Abstract: Disclosed is an apparatus having a detector for an iterative LDPC-coded MIMO-OFDM system, where the detector is configured to use a structured irregular LDPC code in conjunction with a belief propagation algorithm. Also disclosed is an apparatus having a detector for a structured irregular LDPC-coded MIMO-OFDM system, where the detector is configured to use an iterative Recursive Least Squares-based data detection and channel estimation technique. Corresponding methods and computer program products are also disclosed.

52 citations

Journal ArticleDOI
TL;DR: constructive approximation theorems are given which show that under certain conditions, the standard Nadaraya-Watson (1964) regression estimate can be considered a specially regularized form of radial basis function networks (RBFNs), and it is deduced that regularized RBFNs are m.i.s., consistent, like the NWRE for the one-step-ahead prediction of Markovian nonstationary, nonlinear autoregressive time series generated by an i.d.
Abstract: In this paper, constructive approximation theorems are given which show that under certain conditions, the standard Nadaraya-Watson (1964) regression estimate (NWRE) can be considered a specially regularized form of radial basis function networks (RBFNs). From this and another related result, we deduce that regularized RBFNs are m.s., consistent, like the NWRE for the one-step-ahead prediction of Markovian nonstationary, nonlinear autoregressive time series generated by an i.i.d. noise processes. Additionally, choosing the regularization parameter to be asymptotically optimal gives regularized RBFNs the advantage of asymptotically realizing minimum m.s. prediction error. Two update algorithms (one with augmented networks/infinite memory and the other with fixed-size networks/finite memory) are then proposed to deal with nonstationarity induced by time-varying regression functions. For the latter algorithm, tests on several phonetically balanced male and female speech samples show an average 2.2-dB improvement in the predicted signal/noise (error) ratio over corresponding adaptive linear predictors using the exponentially-weighted RLS algorithm. Further RLS filtering of the predictions from an ensemble of three such RBFNs combined with the usual autoregressive inputs increases the improvement to 4.2 dB, on average, over the linear predictors.

52 citations

Journal ArticleDOI
TL;DR: A generalized RLS (GRLS) model is proposed which includes a general decay term in the energy function for the training of feedforward neural networks and four different weight decay functions, namely, the quadratic weight decay, the constant weight decay and the newly proposed multimodal and quartic weight decay are discussed.
Abstract: Recursive least square (RLS) is an efficient approach to neural network training. However, in the classical RLS algorithm, there is no explicit decay in the energy function. This will lead to an unsatisfactory generalization ability for the trained networks. In this paper, we propose a generalized RLS (GRLS) model which includes a general decay term in the energy function for the training of feedforward neural networks. In particular, four different weight decay functions, namely, the quadratic weight decay, the constant weight decay and the newly proposed multimodal and quartic weight decay are discussed. By using the GRLS approach, not only the generalization ability of the trained networks is significantly improved but more unnecessary weights are pruned to obtain a compact network. Furthermore, the computational complexity of the GRLS remains the same as that of the standard RLS algorithm. The advantages and tradeoffs of using different decay functions are analyzed and then demonstrated with examples. Simulation results show that our approach is able to meet the design goals: improving the generalization ability of the trained network while getting a compact network.

51 citations

Journal ArticleDOI
TL;DR: A multiple-forgetting-factor (MFF) version of the RLS adaptive tracking algorithm is presented, that requires no prior knowledge of these aforementioned source statistics or noise statistics.
Abstract: An acoustic vector-sensor (a.k.a. vector-hydrophone) is composed of three acoustic velocity-sensors, plus a collocated pressure-sensor, all collocated in space. The velocity-sensors are identical, but orthogonally oriented, each measuring a different Cartesian component of the three-dimensional particle-velocity field. This acoustic vector-sensor offers an azimuth-elevation response that is invariant with respect to the source's center frequency or bandwidth. This acoustic vector-sensor is adopted here for recursive least-squares (RLS) adaptation, to track a single mobile source, in the absence of any multipath fading and any directional interference. A formula is derived to preset the RLS forgetting factor, based on the prior knowledge of only the incident signal power, the incident source's spatial random walk variance, and the additive noise power. The work presented here further advances a multiple-forgetting-factor (MFF) version of the RLS adaptive tracking algorithm, that requires no prior knowledge of these aforementioned source statistics or noise statistics. Monte Carlo simulations demonstrate the tracking performance and computational load of the proposed algorithms.

51 citations

Journal ArticleDOI
TL;DR: A bilinear recursive least squares (BRLS) adaptive filter is proposed and integrated into a sliding-mode position observer to suppress the dominant harmonic components in the estimated back EMF and as a result, the accuracy of the estimated rotor position can be greatly improved.
Abstract: In the back electromotive force (EMF)-based sensorless control of interior permanent magnet synchronous motor (IPMSM), the inverter nonlinearity and flux linkage spatial harmonics will possibly give rise to (6 k ± 1)th harmonics in the estimated back EMF, especially the fifth and seventh harmonics. Those harmonics will consequently introduce (6 k )th harmonic ripples to the estimated rotor position, especially the sixth harmonic component. In order to solve this problem, a bilinear recursive least squares (BRLS) adaptive filter is proposed and integrated into a sliding-mode position observer to suppress the dominant harmonic components in the estimated back EMF and as a result, the accuracy of the estimated rotor position can be greatly improved. A unique feature of the BRLS adaptive filter is its ability to track and suppress the specified harmonic components in different steady state and dynamic operational conditions. The proposed method can compensate for harmonic ripples caused by the inverter nonlinearity and machine spatial harmonics at the same time; this method is also robust to machine parameter variation, and the BRLS algorithm itself is machine parameter independent. The implementation of the proposed BRLS filter in the sensorless control of IPMSM is explained in details in this paper. The enhanced drive performances using the BRLS filter have been thoroughly validated in different steady state and dynamic operational conditions on a 1.5-kW IPMSM sensorless drive.

51 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022104
2021172
2020228
2019234
2018237