A Complex Varying-Parameter Convergent-Differential Neural-Network for Solving Online Time-Varying Complex Sylvester Equation.
IEEE Trans Cybern
; 49(10): 3627-3639, 2019 Oct.
Article
en En
| MEDLINE
| ID: mdl-29994668
ABSTRACT
A novel recurrent neural network, which is named as complex varying-parameter convergent-differential neural network (CVP-CDNN), is proposed in this paper for solving the time-varying complex Sylvester equation. Two kinds of CVP-CDNNs (i.e., CVP-CDNN Type I and Type II) are illustrated and proved to be effective. The proposed CVP-CDNNs can achieve super-exponential performance if the linear activation function is used. Some activation functions are considered for searching the better performance of the CVP-CDNN and the finite time convergence property of the CVP-CDNN with sign-bi-power activation function is testified. The convergence time of the CVP-CDNN with sign-bi-power activation function is shorter than complex fixed-parameter convergent-differential neural network (CFP-CDNN). Moreover, compared with traditional CFP-CDNN, better convergence performances of novel CVP-CDNN are verified by computer simulation comparisons.
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01-internacional
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MEDLINE
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En
Revista:
IEEE Trans Cybern
Año:
2019
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Article