least-mean-squares algorithms
Kernel methods for short-term spatio-temporal wind prediction
... kernel least mean squares algorithm and kernel recursive least squares algorithm are introduced and used to produce 1 to 6 hour-ahead predictions of wind speed at six locations in the ...
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Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery
... 2.0 in our experimental setting, and also only for a subset of the comparison algorithms, in particular for Matrix ALPS II, ASD, AltMin. The empirical probability of 1 is only reached for some of the IRLS methods, ...
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TO IMPLEMENT LEAST MEAN SQUARES USING INCREASING THE SECURITY OF BIG DATA
... classification algorithms for filtering phishing have been proposed in the literature, the scale and sophistication of phishing attacks have continued to increase ...classifier algorithms in a multi-tier ...
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Active control of time varying broadband noise and vibrations with parallel fast array recursive least squares filters using online system identification of the secondary transfer path
... three algorithms based on the modified filtered reference least mean squares (mod fxLMS) scheme (Elliott, 2001; Bjarnason, 1992) and using an identification noise signal, v, for online system ...
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A Constrained Least Squares Approach to Mobile Positioning: Algorithms and Optimality
... Figure 5 shows the MSREs with TDOA-AOA hybrid mea- surements, where the disturbances in the same type of mea- surements had identical power with zero mean, and they were uncorrelated with each other. It can be ...
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Synthesis of Adaptive Uniform Circular Array Using Normalized Fractional Least Mean Squares Algorithm
... Stochastic-gradient algorithms led to the development of adaptive ...different algorithms with variations in complexity and performance ...gradient algorithms basically serve two ...[6]. ...
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High performance numerical algorithms and software for structured total least squares
... The deconvolution problem is more realistic (and more challenging) when the data a, b is perturbed. We assume that m > n , so that the system of equations (11) is overdetermined. Because both a and b are perturbed and ...
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Family of state space least mean power of two based algorithms
... employ least squares solution ...space least mean square (SSLMS) ...adaptive algorithms which has much lesser computational cost as compared to the existing state space model-based ...
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Performance Analysis of Adaptive Filters for Denoising of ECG Signals
... LMS algorithms, RLS algorithm has a faster convergence speed and do not exhibit eigen value spread ...of least squares for automatically adjusting the coefficient of a FIR filter without invoking ...
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INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES MANAGEMENT STUDIES OF BEAM FORMING ALGORITHMS FOR SMART ANTENNA SYSTEMS IN WIRELESS COMMUNICATION APPLICATION
... Adaptive Algorithms for beam forming for Smart Antenna System has been extensively studied in this research ...sequence algorithms like Recursive Least Squares (RLS) and Least ...
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The Dual of the Least Squares Method
... the least-squares (LS) ...the least-squares method came to ...at least, not treated by any one in a manner worthy its importance; since it assuredly commended it- self to mathematicians ...
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Approximate Least Squares Accelerator
... Gillani et al. [4] improved the ARC framework described above by introducing adaptive statistical ap- proximation model (ASAM). In addition to the original three parameters of SAM, namely error mean (EM), error ...
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On the Equivalence of the Weighted Least Squares and the Generalised Least Squares Estimators, with Applications to Kernel Smoothing
... The paper establishes the conditions under which the generalised least squares estima- tor of the regression parameters is equivalent to the weighted least squares estimator. The equivalence ...
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Least-Squares Policy Iteration
... Our enthusiasm for the approach presented in this paper is inspired by the least-squares temporal-difference learning algorithm (LSTD) (Bradtke and Barto, 1996). The LSTD algo- rithm is ideal for prediction ...
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Comparative Study of Adaptive Filter Algorithm of a QO-STBC Encoded MIMO CDMA System
... 2.4 Least Mean Square (LMS) Adaptive Filter Algorithm LMS algorithm has become one of the most widely used algorithms in adaptive filtering ...
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Filtered X Least Mean Fourth (FXLMF) and Leaky FXLMF adaptive algorithms
... new algorithms to the LMF family, as was proposed before in the ...LFXLMF algorithms successfully converge under a large range of ...both algorithms to converge under different environments of noise: ...
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Floating point error analysis of recursive least squares and least means squares adaptive filters
... This sequence is a zero mean white independent random process which has a variance related to signal statistics, the weight vector covariance, and the floating point errorso The calculat[r] ...
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Resolution of five-component mixture using mean centering ratio and inverse least squares chemometrics
... Results: The linear concentration ranges were 0 – 25.6, 0 – 15.0, 0 – 15.0, 0 – 45.0 and 0 – 100.0 μ g mL -1 for paracetamol, methylparaben, propylparaben, chlorpheniramine maleate and pseudoephedrine hydrochloride, ...
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Stabilized Least Squares Migration
... The goal of this research is to determine whether or not stabilizing a least squares migration (LSM) is possible using velocity model updates. Traditionally LSM is an unstable process which breaks down in ...
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On the generalization of linear least mean squares estimation to quantum systems with non-commutative outputs
... linear least mean squares estimator given in Theorem physically realizable (for ...whose least mean squares estimator is physically ...
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