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Modeling of complex-valued Wiener systems using B-spline neural network

Hong, X. ORCID: and Chen, S. (2011) Modeling of complex-valued Wiener systems using B-spline neural network. IEEE Transactions on Neural Networks, 22 (5). pp. 818-825. ISSN 1045-9227

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To link to this item DOI: 10.1109/TNN.2011.2119328


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.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:19975
Uncontrolled Keywords:B-spline, De Boor algorithm, Wiener system, complex-valued neural networks, system identification

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