An efficient algorithm is derived for the recursive computation of the filtering and all types of linear leastsquare prediction estimates (fixed-point, fixed-interval, and fixed-lead predictors) of a nonstationary signal vector. It is assumed that the signal is observed in the presence of an additive white noise which can be correlated with the signal. The methodology employed only requires tha...

For SOC (state of charge) assessment techniques based on electrical circuit models, the parameters model are strongly biased by: battery aging, temperature, causing some errors in estimation SOC. One approach to solve this problem is update constantly. We suggest a new algorithm VRLS (variable recursive least squares) 2-resistor-capacitor (RC) network and estimate output voltage. compared squar...

Acoustic array sensor along with Root-MUSIC algorithm is used to estimate the direction of arrival of the acoustic signal emitted by an acoustic target. Three architectures are used to track the target in Cartesian coordinates: (i) digital filter with least square estimation, (ii) linear Kalman filter with least square estimation, and (iii) extended Kalman filter. A comparative evaluation of th...

o enhance the performances of rough-neural networks (R-NNs) in the system identification‎, ‎on the base of emotional learning‎, ‎a new stable learning algorithm is developed for them‎. ‎This algorithm facilitates the error convergence by increasing the memory depth of R-NNs‎. ‎To this end‎, ‎an emotional signal as a linear combination of identification error and its differences is used to achie...

in this paper, we examine target detection in the surveillance channels of fm-based passive bistatic radars (fbpbrs) in the presence of interference signals in the reference channels of these systems. to do so, we propose a new signal conditioning algorithm to remove interference signals form reference channel. simulation results show that the proposed signal conditioning algorithm outperform t...

a weighted linear regression model with impercise response and p-real explanatory variables is analyzed. the lr fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. a least square solution for estimating the parameters of the model is derived. the result are illustrated by the means of some case studies.

An important step in the asynchronous multi-sensor registration problem is to estimate sensor range and azimuth biases from their noisy asynchronous measurements. The estimation problem is generally very challenging due to highly nonlinear transformation between the global and local coordinate systems as well as measurement asynchrony from different sensors. In this paper, we propose a novel no...

unknown noise and artifacts present in medical signals with  non-linear fuzzy filter will be estimate and then removed. an adaptive neuro-fuzzy interference system which has a nonlinear  structure presented  for the noise function prediction by before samples. this paper is about a neuro-fuzzy method to estimate unknown noise of electrocardiogram (ecg) signal. adaptive neural combined with fuzz...

In this paper we propose a new algorithm that estimates on-line the parameters of classical vector linear regression equation Y=Ωθ, where Y∈Rn,Ω∈Rn×q are bounded, measurable signals and θ∈Rq is constant unknown parameters, even when regressor Ω not persistently exciting. Moreover, convergence parameter estimator global exponential given for both, continuous-time discrete-time implementations. A...

The problem of regulating the transmission rate of an available bit rate (ABR) traÆc source in an ATM network is examined. Of particular interest is linear quadratic (LQ) rate regulation based on estimates of the round-trip propagation delay. The round trip delay is estimated using a nonlinear least mean square (NLMS) algorithm. Simulation results are used to demonstrate the method.