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Design and Analysis of New Zeroing Neural Network Models With Improved Finite-Time Convergence for Time-Varying Reciprocal of Complex Matrix.
- Source :
- IEEE Transactions on Industrial Informatics; Jun2020, Vol. 16 Issue 6, p3838-3848, 11p
- Publication Year :
- 2020
-
Abstract
- In this article, two improved finite-time convergent complex-valued zeroing neural network (IFTCVZNN) models are presented and investigated for real-time solution of time-varying reciprocal of complex matrices on account of two equivalent processing ways of complex calculations for nonlinear activation functions. Furthermore, a novel nonlinear activation function is explored to modify the comprehensive performance of such two IFTCVZNN models. Compared with existing complex-valued neural networks converging within the limited time, the proposed IFTCVZNN models with the new activation function have better finite-time convergence and less conservative upper bound. Numerical simulations verify that the maximum of convergence time estimated via Lyapunov stability is theoretically much closer to the actual convergence time. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15513203
- Volume :
- 16
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Industrial Informatics
- Publication Type :
- Academic Journal
- Accession number :
- 142145291
- Full Text :
- https://doi.org/10.1109/TII.2019.2941750