This paper presents a statistical analysis of the least mean square (LMS) algorithm with a zero-memory scaled error function nonlinearity following the adaptive filter output. This structure models saturation effects in active noise and active vibration control systems when the acoustic transducers are driven by large amplitude signals. The problem is first defined as a nonlinear signal estimat...

This research aims to create the most efficient and accurate cab fare prediction system using two machine learning algorithms, Multiple linear Regression algorithm random forest algorithm, compare parameters r-square, Mean Square Error (MSE), Root MSE, RMSLE values evaluate efficiency of algorithm. Considering as group 1 algorithms implemented, 2 process was predict prices get best accuracy alg...

In this paper a 4 x 4 Daubechies transform based authentication technique termed as SADT has been proposed to authenticate gray scale images. The cover image is transformed into the frequency domain using 4 x 4 mask in a row major order using Daubechies transform technique, resulting four frequency subbands AF, HF, VF and DF. One byte of every band in a mask is embedding with two/four bits of s...

<span lang="EN-US">This paper presents the comparison between optimized unscented Kalman filter (UKF) and extended (EKF) for sensorless direct field orientation control induction motor (DFOCIM) drive. The high performance of UKF EKF depends on accurate selection state noise covariance matrices. For this goal, multi objective function genetic algorithm is used to find optimal values main o...

In this paper, a modified estimation algorithm has been developed refers to Covariance Shaping Least Square (CSLS) estimation based on the quantum mechanical concepts and constraints. The algorithm has been applied to Auto Regressive Moving Average (ARMA models with various parameter values. The same models can be applied with Colored Noise which estimates the bias in the parameter and the vali...

Three adaptive multichannel L-lters based on marginal data ordering are proposed. They rely on well-known algorithms for the iterative minimization of the mean square error (MSE), namely, the least mean squares (LMS), the normalized LMS (NLMS), and the LMS-Newton (LMSN) algorithms. We treat both the unconstrained minimization of the MSE and the minimization of the MSE when structural constraint...

We consider the problem of subspace estimation in aBayesian setting. Since we are operating in the Grassmann man-ifold, the usual approach which consists of minimizing the meansquare error (MSE) between the true subspace and its estimatemay not be adequate as the MSE is not the natural metric in theGrassmann manifold , i.e., the set of -dimensional subspacesin . As an al...

Robust diffusion algorithms based on the maximum correntropy criterion(MCC) are developed to address the distributed networks estimation issue in impulsive(long-tailed) noise environments. The cost functions used in distributed network estimation are in general based on the mean square error (MSE) criterion, which is optimal only when the measurement noise is Gaussian. In non-Gaussian situation...

Ahstpact-Convergence properties of a continuously adaptive digital lattice filter. used as a linear predictor are investigated for both an unnormalized and a normalized gradient adaptation algorithm. The PARCOR coefficient mean values and the output mean-square error (MSE) are approximated and a simple model is described which approximates these quantities as functions of time. Calculated curve...

Low-cost estimation of stationary signals and reduced-complexity tracking of nonstationary processes are well motivated tasks than can be accomplished using ad hoc wireless sensor networks (WSNs). To this end, a fully distributed least mean-square (D-LMS) algorithm is developed in this paper, in which sensors exchange messages with single-hop neighbors to consent on the network-wide estimates a...