Individually Weighted Modified Logarithmic Hyperbolic Sine Curvelet Based Recursive FLN for Nonlinear System Identification

Lately, an adaptive exponential functional link network (AEFLN) involving exponential terms integrated with trigonometric functional expansion is being introduced as a linear-in-the-parameters nonlinear filter. However, they exhibit degraded efficacy in lieu of non-Gaussian or impulsive noise interf...

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Veröffentlicht in:Circuits, systems, and signal processing Jg. 44; H. 1; S. 306 - 337
Hauptverfasser: Chikyal, Neetu, Vasundhara, Bhar, Chayan, Kar, Asutosh, Christensen, Mads Graesboll
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.01.2025
Springer Nature B.V
Schlagworte:
Acoustics
Adaptation
Adaptive algorithms
Adaptive systems
Algorithms
Circuits and Systems
Communication
Conjugation
Convergence
Cost function
Effectiveness
Electrical Engineering
Electronics and Microelectronics
Engineering
Exponential functions
Hearing aids
Hyperbolic functions
Identification
Instrumentation
Logarithms
Noise control
Nonlinear filters
Nonlinear systems
Nonlinearity
Optimization techniques
Parameter identification
Parameter modification
Recursive functions
Signal processing
Signal,Image and Speech Processing
Simulation
Sparsity
System identification
Trigonometric functions
Outliers
Nonlinear filter
Impulsive noise
System identification
Recursive
Functional link network
ISSN:0278-081X, 1531-5878
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Zusammenfassung:Lately, an adaptive exponential functional link network (AEFLN) involving exponential terms integrated with trigonometric functional expansion is being introduced as a linear-in-the-parameters nonlinear filter. However, they exhibit degraded efficacy in lieu of non-Gaussian or impulsive noise interference. Therefore, to enhance the nonlinear modelling capability, here is a modified logarithmic hyperbolic sine cost function in amalgamation with the adaptive recursive exponential functional link network. In conjugation with this, a sparsity constraint motivated by a curvelet-dependent notion is employed in the suggested approach. Therefore, this paper presents an individually weighted modified logarithmic hyperbolic sine curvelet-based recursive exponential FLN (IMLSC-REF) for robust sparse nonlinear system identification. An individually weighted adaptation gain is imparted to several coefficients corresponding to the nonlinear adaptive model for accelerating the convergence rate. The weight update rule and the maximum criteria for the convergence factor are being further derived. Exhaustive simulation studies profess the effectiveness of the introduced algorithm in case of varied nonlinearity and for identifying as well as modelling the physical path of the acoustic feedback phenomenon of a behind-the-ear (BTE) hearing aid.
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ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-024-02839-3