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A Variable Parameter Zeroing Neural Network for Resolving Time-Variant Quadratic Minimization with Preferable Performance | IEEE Conference Publication | IEEE Xplore
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A Variable Parameter Zeroing Neural Network for Resolving Time-Variant Quadratic Minimization with Preferable Performance


Abstract:

The variable parameter recurrent neural network has powerful abilities to resolve the various time-variant problems. In this work, a new-type neural network model to hand...Show More

Abstract:

The variable parameter recurrent neural network has powerful abilities to resolve the various time-variant problems. In this work, a new-type neural network model to handle the time-variant quadratic minimization issue is proposed and researched. Distinct from the generalized original zeroing neural network (OZNN) model, the transfer function part of the proposed variable parameter zeroing neural network (VPZNN) model use the sign-bi-power function, and the parameter part use the specially constructed time-variant parameter. Finally, numerical simulation is carried out with and without noise. The simulation results exhibit that the proposed VPZNN model has preferable convergence capability and robustness than the OZNN model and the finite-time Zhang neural network (FTZNN) model.
Date of Conference: 14-16 August 2020
Date Added to IEEE Xplore: 26 August 2020
ISBN Information:
Electronic ISSN: 2573-3311
Conference Location: Dali, China

References

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