Stochastic gradient-adaptive complex-valued nonlinear neural adaptive filters with a gradient-adaptive step size.
A class of variable step-size learning algorithms for complex-valued nonlinear adaptive finite impulse response (FIR) filters is proposed. To achieve this, first a general complex-valued nonlinear gradient-descent (CNGD) algorithm with a fully complex nonlinear activation function is derived. To improve the convergence and robustness of CNGD, we further introduce a gradient-adaptive step size to give a class of variable step-size CNGD (VSCNGD) algorithms. The analysis and simulations show the proposed class of algorithms exhibiting fast convergence and being able to track nonlinear and nonstationary complex-valued signals. To support the derivation, an analysis of stability and computational complexity of the proposed algorithms is provided. Simulations on colored, nonlinear, and real-world complex-valued signals support the analysis.
Goh
Su Lee
SL
Mandic
Danilo P
DP
D000465:Algorithms; D003198:Computer Simulation; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D018401:Sample Size; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes