Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems

Abstract

Many communication signal processing applications involve modeling and inverting complex-valued (CV) Hammerstein systems. We develop a new CV B-spline neural network approach for efficient identification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein model. In particular, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for identifying the CV linear dynamic model’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic model’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated model. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian Newton algorithm, which naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first-order derivative recursions. The effectiveness of our approach is demonstrated using the application to equalization of Hammerstein channels.

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