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

Chen, S., Hong, X., Gao, J. and Harris, C.J. (2014) Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems. IEEE Transactions on Neural Networks and Learning Systems, 25 (9). pp. 1673-1685. ISSN 2162-237X

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To link to this item DOI: 10.1109/TNNLS.2014.2298535

Abstract/Summary

Many communication signal processing applications involve modelling and inverting complex-valued (CV) Hammerstein systems. We develops 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. Specifically, 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 equalisation of Hammerstein channels.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:36486
Uncontrolled Keywords:Complex-valued neural networks, Hammerstein model, Wiener model, B-spline neural networks, De Boor algorithm, equalisation
Publisher:IEEE Computational Intelligence Society

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