Numeric Variable Forgetting Factor RLS Algorithm for Second-Order Volterra Filtering

Kohli, Amit Kumar; Rai, Amrita

doi:10.1007/s00034-012-9445-7

Cited by 30 publications

(8 citation statements)

References 14 publications

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“…Some alternative approaches for the scale factor adaptation can be found in the literature [ 15 , 16 , 24 ]. Moreover, the application of the EPE-based VFF for solving different practical problems is also discussed in the literature [ 12 , 17 – 20 , 29 , 30 ]. However, similarly to sample mean and sample variance, the standard EPE approach is non-robust towards outliers [ 21 – 23 ].…”

Section: Problem Formulationmentioning

confidence: 99%

Robust adaptive filtering using recursive weighted least squares with combined scale and variable forgetting factors

Kovačevíc

Banjac²,

Kovačević

2016

EURASIP J. Adv. Signal Process.

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In this paper, a new adaptive robustified filter algorithm of recursive weighted least squares with combined scale and variable forgetting factors for time-varying parameters estimation in non-stationary and impulsive noise environments has been proposed. To reduce the effect of impulsive noise, whether this situation is stationary or not, the proposed adaptive robustified approach extends the concept of approximate maximum likelihood robust estimation, the so-called M robust estimation, to the estimation of both filter parameters and noise variance simultaneously. The application of variable forgetting factor, calculated adaptively with respect to the robustified prediction error criterion, provides the estimation of time-varying filter parameters under a stochastic environment with possible impulsive noise. The feasibility of the proposed approach is analysed in a system identification scenario using finite impulse response (FIR) filter applications.

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Section: Problem Formulationmentioning

confidence: 99%

Robust adaptive filtering using recursive weighted least squares with combined scale and variable forgetting factors

Kovačevíc

Banjac²,

Kovačević

2016

EURASIP J. Adv. Signal Process.

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“…At this point, the forgetting factor λ plays an important role in the behavior of the RLS algorithm in terms of convergence, misalignment, and stability. λ is fixed in the classical FFRLS algorithm, usually called the fixed forgetting factor (FFF), and its value ranges from 0 to 1 [13]. It is known that if λ is close to 1, then the algorithm achieves faster convergence but with reduced tracking ability.…”

Section: Recursive Least Squares Methods With Fixed Forgettingmentioning

confidence: 99%

“…e zeroorder keeper uses constant extrapolation and holds the sampling value u(kT) at the moment kT to the moment (k + 1)T. Meanwhile, the sample value changes from u(kT) to u[(k + 1)T] and obtains a stepped output signal that contains higher harmonics. With increasing frequency, the amplitude of the zero-order keeper decays; thus, the zeroorder keeper possesses the characteristics of low-pass filtering [13]. Discretizing the combined integrating process of type (a),…”

Section: Discretization Of Combined Integrating Processmentioning

confidence: 99%

A Method for Parameter Identification of Combined Integrating Systems

Fan

Ren

Chen

et al. 2020

Mathematical Problems in Engineering

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This manuscript presents a novel structure of combined integration in the process industry and proposes an efficient method for identifying its parameters. Combined integrating processes are delay-time processes that widely exist in the industry. Conventional identification methods have a low-identification accuracy, a large vibration amplitude of the identification curve, and a poor effect for this kind of process. In this paper, a new variable forgetting factor recursive least squares method was adopted to ameliorate this problem. The method could quickly track the mutation of the ideal parameters of the process and accurately identify which of the parameters has high precision, small oscillation, and a smooth curve. The simulation results indicate that the proposed method is a significant improvement compared to the ordinary recursive least squares method and the recursive least squares with a fixed forgetting factor method, and a concise program can be verified. The experimental simulation based on the actual cut tobacco rebaking industrial process shows that the proposed method has improved identification precision and the best following effect.

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“…In the field of parameter identification for time-varying systems, identification methods with recursive least squares algorithms with a forgetting factor are common [18,21,31]. For example, Paleologu et al [35] proposed a variable forgetting factor RLS algorithm for time-varying systems identification, and Leung et al [19] presented a new variable forgetting factor RLS adaptive algorithm based on the mean square error.…”

Section: Introductionmentioning

confidence: 99%

A Multi-innovation Recursive Least Squares Algorithm with a Forgetting Factor for Hammerstein CAR Systems with Backlash

Shi

Wang

2016

Circuits Syst Signal Process

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This study addresses the identification of Hammerstein CAR systems with backlash, where the nonlinear backlash is described as one regression identification model using a two switching function mathematical model. In such a case, the Hammerstein CAR systems with backlash can be transformed into a piecewise linearized model. Then, a novel multi-innovation recursive least squares algorithm with a forgetting factor is applied to estimate the parameters of the proposed model. Finally, numerical examples are presented to test the performance of the proposed algorithm.

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Numeric Variable Forgetting Factor RLS Algorithm for Second-Order Volterra Filtering

Cited by 30 publications

References 14 publications

Robust adaptive filtering using recursive weighted least squares with combined scale and variable forgetting factors

Robust adaptive filtering using recursive weighted least squares with combined scale and variable forgetting factors

A Method for Parameter Identification of Combined Integrating Systems

A Multi-innovation Recursive Least Squares Algorithm with a Forgetting Factor for Hammerstein CAR Systems with Backlash

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