Modeling of complex-valued Wiener systems using B-spline neural network
Hong, Xia and Chen, Sheng (2011) Modeling of complex-valued Wiener systems using B-spline neural network. IEEE Transactions on Neural Networks, 22, (5), 818-825.
- Published Version
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 Bspline 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.
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||09 May 2011 14:05|
|Last Modified:||25 Aug 2012 02:33|
|Contributors:||Hong, Xia (Author)
Chen, Sheng (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||1|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Actions (login required)