Adaptive Coefficient Designs for Nonlinear Activation Function and Its Application to Zeroing Neural Network for Solving Time-Varying Sylvester Equation

2020 ◽  
Vol 357 (14) ◽  
pp. 9909-9929
Author(s):  
Zhen Jian ◽  
Lin Xiao ◽  
Kenli Li ◽  
Qiuyue Zuo ◽  
Yongsheng Zhang
Keyword(s):  
Neural Network ◽  
Activation Function ◽  
Sylvester Equation ◽  
Time Varying ◽  
Nonlinear Activation Function

A New Varying-Parameter Recurrent Neural-Network for Online Solution of Time-Varying Sylvester Equation

2018 ◽  
Vol 48 (11) ◽  
pp. 3135-3148 ◽  
Author(s):  
Zhijun Zhang ◽  
Lunan Zheng ◽  
Jian Weng ◽  
Yijun Mao ◽  
Wei Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Sylvester Equation ◽  
Time Varying

scholarly journals An Improved Complex-Valued Recurrent Neural Network Model for Time-Varying Complex-Valued Sylvester Equation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 19291-19302 ◽  
Author(s):  
Lei Ding ◽  
Lin Xiao ◽  
Kaiqing Zhou ◽  
Yonghong Lan ◽  
Yongsheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Sylvester Equation ◽  
Time Varying ◽  
Complex Valued

Relating the Slope of the Activation Function and the Learning Rate Within a Recurrent Neural Network

1999 ◽  
Vol 11 (5) ◽  
pp. 1069-1077 ◽  
Author(s):  
Danilo P. Mandic ◽  
Jonathon A. Chambers
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Network ◽  
Recurrent Neural Networks ◽  
Degrees Of Freedom ◽  
Learning Algorithm ◽  
Activation Function ◽  
Learning Rate ◽  
Optimization Task ◽  
Nonlinear Activation Function

A relationship between the learning rate η in the learning algorithm, and the slope β in the nonlinear activation function, for a class of recurrent neural networks (RNNs) trained by the real-time recurrent learning algorithm is provided. It is shown that an arbitrary RNN can be obtained via the referent RNN, with some deterministic rules imposed on its weights and the learning rate. Such relationships reduce the number of degrees of freedom when solving the nonlinear optimization task of finding the optimal RNN parameters.

scholarly journals Residual Error Feedback Zeroing Neural Network for Solving Time-Varying Sylvester Equation

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Kunjian Li ◽  
Chengze Jiang ◽  
Xiuchun Xiao ◽  
Haoen Huang ◽  
Yongjiang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Residual Error ◽  
Sylvester Equation ◽  
Time Varying ◽  
Error Feedback
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