his work presents an effective solution to reduce the computational complexity order of
behavioral model generation for reflective semiconductor optical amplifier (RSOA). The proposed
model is based on a complex valued (CV) neural network (CVNN) structure, using polar CV basis
functions architecture. The CV model parameters are extracted by means of nonlinear complex-domain
Levenberg-Marquardt algorithm, from recorded experimental 20 Msymbol/s 64-quadrature amplitude
modulation (QAM) input-output data. The evaluation results of polar CVNN model proves to be more
adequate to accurately describe the nonlinear dynamic magnitude and phase distortions of RSOA,
compared to double-input double-output real-valued neural network (RVNN) rectangular structure.
Additionally, significant reduction of the computational cost is achieved in comparison to the RVNN
approach .