Complex time-dependent Lyapunov equation (CTDLE), as an important means of stability analysis of control systems, has been extensively employed in mathematics and engineering application fields. Recursive neural networks (RNNs) have been reported as an effective method for solving CTDLE. In the previous work, zeroing neural networks (ZNNs) have been established to find the accurate solution of time-dependent Lyapunov equation (TDLE) in the noise-free conditions. However, noises are inevitable in the actual implementation process. In order to suppress the interference of various noises in practical applications, in this paper, a complex noise-resistant ZNN (CNRZNN) model is proposed and employed for the CTDLE solution. Additionally, the conv...
A new adaptive backpropagation (BP) algorithm based on Lyapunov stability theory for neural networks...
Because of the strong dynamic behavior and computing power, zeroing neural networks (ZNNs) have been...
© 2020 The Author(s). Published by IOP Publishing Ltd. We train an artificial neural network which d...
Complex time-dependent Lyapunov equation (CTDLE), as an important means of stability analysis of con...
Complex-valued time-dependent matrix inversion (TDMI) is extensively exploited in practical industri...
Zeroing neural network (ZNN) is a powerful tool to address the mathematical and optimization problem...
The solving of quadratic matrix equations is a fundamental issue which essentially exists in the opt...
Defining efficient families of recurrent neural networks (RNN) models for solving time-varying nonli...
This research introduces three novel zeroing neural network (ZNN) models for addressing the time-var...
Sylvester equation is often applied to various fields, such as mathematics and control systems due t...
The time-varying quadratic miniaturization (TVQM) problem, as a hotspot currently, urgently demands ...
Dynamic complex matrix inversion (DCMI) problems frequently arise in the territories of mathematics ...
In this work, two robust zeroing neural network (RZNN) models are presented for online fast solving ...
A new approach for the adaptive algorithm of a fully connected recurrent neural network (RNN) based ...
For quadratic programming (QP), it is usually assumed that the solving process is free of measuremen...
A new adaptive backpropagation (BP) algorithm based on Lyapunov stability theory for neural networks...
Because of the strong dynamic behavior and computing power, zeroing neural networks (ZNNs) have been...
© 2020 The Author(s). Published by IOP Publishing Ltd. We train an artificial neural network which d...
Complex time-dependent Lyapunov equation (CTDLE), as an important means of stability analysis of con...
Complex-valued time-dependent matrix inversion (TDMI) is extensively exploited in practical industri...
Zeroing neural network (ZNN) is a powerful tool to address the mathematical and optimization problem...
The solving of quadratic matrix equations is a fundamental issue which essentially exists in the opt...
Defining efficient families of recurrent neural networks (RNN) models for solving time-varying nonli...
This research introduces three novel zeroing neural network (ZNN) models for addressing the time-var...
Sylvester equation is often applied to various fields, such as mathematics and control systems due t...
The time-varying quadratic miniaturization (TVQM) problem, as a hotspot currently, urgently demands ...
Dynamic complex matrix inversion (DCMI) problems frequently arise in the territories of mathematics ...
In this work, two robust zeroing neural network (RZNN) models are presented for online fast solving ...
A new approach for the adaptive algorithm of a fully connected recurrent neural network (RNN) based ...
For quadratic programming (QP), it is usually assumed that the solving process is free of measuremen...
A new adaptive backpropagation (BP) algorithm based on Lyapunov stability theory for neural networks...
Because of the strong dynamic behavior and computing power, zeroing neural networks (ZNNs) have been...
© 2020 The Author(s). Published by IOP Publishing Ltd. We train an artificial neural network which d...