Journal ArticleDOI
Recursive inverse adaptive filtering algorithm
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TLDR
A new FIR adaptive filtering algorithm based on the Quasi-Newton (QN) optimization algorithm that uses a variable step-size in the coefficient update equation that leads to an improved performance.About:
This article is published in Digital Signal Processing.The article was published on 2011-07-01. It has received 53 citations till now. The article focuses on the topics: Recursive least squares filter & Gaussian noise.read more
Citations
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Journal ArticleDOI
Back-propagation algorithm with variable adaptive momentum
TL;DR: A novel machine learning classifier by deriving a new adaptive momentum back-propagation (BP) artificial neural networks algorithm to improve its convergence behavior in both sides, accelerate the convergence process for accessing the optimum steady-state and minimizing the error misadjustment to improve the recognized patterns superiorly.
Journal ArticleDOI
Performance Analysis of the Auxiliary Model-Based Stochastic Gradient Parameter Estimation Algorithm for State-Space Systems with One-Step State Delay
Feng Ding,Ya Gu +1 more
TL;DR: An auxiliary model-based stochastic gradient parameter estimation algorithm is presented and its convergence for the input–output representation for state-space systems with one-step delays is studied by means of the auxiliary model identification idea.
Journal ArticleDOI
FPGA implementation of modified error normalized LMS adaptive filter for ECG noise removal
TL;DR: A delayed error normalized LMS (DENLMS) adaptive filter is studied with pipelining architecture to remove the white Gaussian noise from ECG signal and the performance of pipelined DENLMS algorithm is compared with ENLMS and DNLMS algorithms.
Journal ArticleDOI
Joint Estimation of States and Parameters for an Input Nonlinear State-Space System with Colored Noise Using the Filtering Technique
Xuehai Wang,Feng Ding +1 more
TL;DR: A combined state and parameter estimation algorithm is developed for identifying the state-space system by using the data filtering and the over-parameterization technique to transform the original nonlinear state- space system into two identification models with filtered states.
Journal ArticleDOI
Filtering based recursive least squares algorithm for Hammerstein FIR-MA systems
TL;DR: The proposed filtering-based recursive least squares algorithm can estimate the noise and system models of Hammerstein finite impulse response systems more accurately and has a higher computational efficiency than the recursive most squares algorithm.
References
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Book
Adaptive Filter Theory
TL;DR: In this paper, the authors propose a recursive least square adaptive filter (RLF) based on the Kalman filter, which is used as the unifying base for RLS Filters.
Book
Kalman Filtering and Neural Networks
TL;DR: This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear.
Book
Adaptive Filters
TL;DR: Adaptive Filters offers a fresh, focused look at the subject in a manner that will entice students, challenge experts, and appeal to practitioners and instructors.
Book
Adaptive Filtering: Algorithms and Practical Implementation
TL;DR: Adaptive Filtering: Algorithms and Practical Implementation may be used as the principle text for courses on the subject, and serves as an excellent reference for professional engineers and researchers in the field.
Journal ArticleDOI
Adaptive tracking of linear time-variant systems by extended RLS algorithms
TL;DR: This work exploits the one-to-one correspondences between the recursive least-squares (RLS) and Kalman variables to formulate extended forms of the RLS algorithm that are applicable to a system identification problem and the tracking of a chirped sinusoid in additive noise.
Related Papers (5)
Parameter estimation for Hammerstein CARARMA systems based on the Newton iteration
Junhong Li,Junhong Li +1 more