Convergence analysis of a discrete-time recurrent neural network to perform quadratic real optimization with bound constraints

1998 ◽  
Vol 9 (6) ◽  
pp. 1344-1351 ◽  
Author(s):  
M.J. Perez-Ilzarbe
Keyword(s):  
Neural Network ◽  
Convergence Analysis ◽  
Discrete Time ◽  
Recurrent Neural Network ◽  
Bound Constraints

Convergence Theory for Unified Discrete-Time RNNs with Quasi-Symmetric Connection

2014 ◽  
Vol 538 ◽  
pp. 167-170
Author(s):  
Hui Zhong Mao ◽  
Chen Qiao ◽  
Wen Feng Jing ◽  
Xi Chen ◽  
Jin Qin Mao
Keyword(s):  
Neural Network ◽  
Global Convergence ◽  
Discrete Time ◽  
Recurrent Neural Network ◽  
Symmetric Matrix ◽  
Network Models ◽  
Convergence Theory ◽  
Connection Matrix ◽  
Neural Network Models ◽  
Symmetric Connection

This paper presents the global convergence theory of the discrete-time uniform pseudo projection anti-monotone network with the quasi–symmetric matrix, which removes the connection matrix constraints. The theory widens the range of applications of the discrete–time uniform pseudo projection anti–monotone network and is valid for many kinds of discrete recurrent neural network models.

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