Perception-Based ℓp-Norm Minimization Approach for Nonlinear System Identification in GGD Noise

Nonlinear system identification is an important and fundamental problem in many practical applications. It becomes more challenging when the noise is non-Gaussian. Inspired by the cognitive dynamic system concept, we propose a perception-based ℓ p -norm minimization approach for nonlinear system ide...

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Bibliographic Details
Published in:Circuits, systems, and signal processing systems, and signal processing, 2017-08, Vol.36 (8), p.3426-3437
Main Authors: Wen, Fuxi, Wang, Zhongmin
Format: Article
Language:English
Subjects:
Circuits and Systems
Computer simulation
Electrical Engineering
Electronics and Microelectronics
Engineering
Feedback loops
Gaussian distribution
Instrumentation
Noise
Nonlinear systems
Normal distribution
Optimization
Perception
Short Paper
Signal,Image and Speech Processing
System identification
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Summary:Nonlinear system identification is an important and fundamental problem in many practical applications. It becomes more challenging when the noise is non-Gaussian. Inspired by the cognitive dynamic system concept, we propose a perception-based ℓ p -norm minimization approach for nonlinear system identification in generalized Gaussian distribution noise environments. Volterra model is utilized to describe the nonlinear system. The proposed cognitive algorithm incorporates a closed feedback loop between perceptions and actions to the environments. Computer simulations have been carried out to illustrate the effectiveness of the proposed method.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-016-0454-9