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Robust Adaptive Filtering Based on Exponential Functional Link Network: Analysis and Application
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DOI：10.1109/TCSII.2021.3056708.png)
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Abstract
En 中文
The exponential functional link network (EFLN) has been recently investigated and applied to nonlinear filtering. This brief proposes an adaptive EFLN filtering algorithm based on a novel inverse square root (ISR) cost function, called the EFLN-ISR algorithm, whose learning capability is robust under impulsive interference. The steady-state performance of EFLN-ISR is rigorously derived and then confirmed by numerical simulations. Moreover, the validity of the proposed EFLN-ISR algorithm is justified by the actually experimental results with the application to hysteretic nonlinear system identification.
Keywords:
Steady-state
Cost function
Interference
Nonlinear systems
Robustness
Adaptation models
Convergence
Exponential functional link network
hysteretic system identification
impulsive interference
inverse square root
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