Modelling and inverting complex-valued wiener systems

Hong, Xia; Chen, Sheng; Harris, C.J.

doi:10.1109/ijcnn.2012.6252811

Cited by 2 publications

(1 citation statement)

References 23 publications

(23 reference statements)

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“…Cousseau et al proposed a two-dimensional simplified canonical piecewise linear filter to identify complex-valued nonlinear Wiener model [14]. In order to identify complex-valued Wiener system more accurately, Hong et al proposed a complex-valued B-spline neural network method [15]. The black-box modeling methods of complex-valued systems have some disadvantages, such as difficult structure design, unclear internal interpretation and so on.…”

Section: Introductionmentioning

confidence: 99%

CSE: Complex-Valued System With Evolutionary Algorithm

et al. 2019

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Complex-valued system identification has the ability to provide the basis for system analysis. So as to demonstrate the potential and internal mechanism of the complex-valued system, this paper proposes a novel complex-valued hybrid evolutionary (CSE) algorithm to optimize the complex-valued expression model (CEM). Complex-valued gene expression programming (CVGEP) is proposed to optimize the architectures of the CEM. Complex-valued water wave optimization (CVWWO) is first proposed to optimize complex-valued coefficients and constants of the CEM. The two artificial complex-valued function approximation problems and non-minimum phase equalization problem are utilized to test the performance of the proposed algorithm. The results demonstrate that this method could identify complex-valued systems more correctly than complex-valued neural networks, which could obtain above 90% smaller mean-squared error (MSE) performance than other complex-valued models. With 10% Gaussian white noise, the CSE could identify accurate complex-valued structure and parameters containing coefficients and constants. The CVWWO has better convergence performance than complex-valued particle swarm optimization and crow search algorithm.INDEX TERMS Complex-valued expression model, gene expression programming, water wave optimization.

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Section: Introductionmentioning

confidence: 99%