Title :
Modeling of Complex-Valued Wiener Systems Using B-Spline Neural Network
Author :
Hong, Xia ; Chen, Sheng
Author_Institution :
Sch. of Syst. Eng., Univ. of Reading, Reading, UK
fDate :
5/1/2011 12:00:00 AM
Abstract :
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.
Keywords :
least squares approximations; neural nets; parameter estimation; splines (mathematics); B-spline curve; De Boor algorithm; Gauss-Newton algorithm; communication systems; complex-valued B-spline neural network; complex-valued Wiener systems; complex-valued nonlinear static function; first-order derivatives recursion; least squares parameter initialization; nonlinear high-power amplifier model; parameter estimation; tensor product; univariate B-spline neural networks; Artificial neural networks; Biological system modeling; Numerical models; Polynomials; Signal processing algorithms; Spline; B-spline; De Boor algorithm; Wiener system; complex-valued neural networks; system identification; Algorithms; Artificial Intelligence; Computer Simulation; Mathematical Concepts; Models, Theoretical; Neural Networks (Computer); Nonlinear Dynamics; Normal Distribution; Pattern Recognition, Automated;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2011.2119328