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Modeling of Complex-Valued Wiener Systems Using B-Spline Neural Network

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2 Author(s)
Xia Hong ; Sch. of Syst. Eng., Univ. of Reading, Reading, UK ; Sheng Chen

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches.

Published in:

Neural Networks, IEEE Transactions on  (Volume:22 ,  Issue: 5 )