## Efficient Nonlinear Wiener Model Identification Using a Complex-Valued Simplicial Canonical Piecewise Linear Filter

- Juan E. Cousseau, JosÃ© Luis Figueroa, Stefan Werner, Timo I. Laakso
- IEEE Transactions on Signal Processing
- 2007

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@article{Hanna2003AFA, title={A fully adaptive normalized nonlinear gradient descent algorithm for complex-valued nonlinear adaptive filters}, author={Andrew I. Hanna and Danilo P. Mandic}, journal={IEEE Trans. Signal Processing}, year={2003}, volume={51}, pages={2540-2549} }

- Published 2003 in IEEE Trans. Signal Processing
DOI:10.1109/TSP.2003.816878

A fully adaptive normalized nonlinear complex-valued gradient descent (FANNCGD) learning algorithm for training nonlinear (neural) adaptive finite impulse response (FIR) filters is derived. First, a normalized nonlinear complex-valued gradient descent (NNCGD) algorithm is introduced. For rigour, the remainder of the Taylor series expansion of the instantaneous output error in the derivation of NNCGD is made adaptive at every discrete time instant using a gradient-based approach. This results inâ€¦Â CONTINUE READING

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