A New Stability Condition of Discrete Hopfield Neural Networks with Weight Function Matrix

2008 ◽  
Author(s):  
Ying Zhang ◽  
Jun Li ◽  
Zheng-wang Ye
Keyword(s):  
Neural Networks ◽  
Weight Function ◽  
Stability Condition ◽  
Hopfield Neural Networks ◽  
Function Matrix

scholarly journals Stability Analysis of Discrete Hopfield Neural Networks with the Nonnegative Definite Monotone Increasing Weight Function Matrix

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Jun Li ◽  
Yongfeng Diao ◽  
Mingdong Li ◽  
Xing Yin
Keyword(s):  
Neural Networks ◽  
Weight Function ◽  
Stable State ◽  
Hopfield Neural Networks ◽  
Definite Function ◽  
Parallel Model ◽  
Weight Changes ◽  
The Stability ◽  
Nonnegative Definite ◽  
Function Matrix

The original Hopfield neural networks model is adapted so that the weights of the resulting network are time varying. In this paper, the Discrete Hopfield neural networks with weight function matrix (DHNNWFM) the weight changes with time, are considered, and the stability of DHNNWFM is analyzed. Combined with the Lyapunov function, we obtain some important results that if weight function matrix (WFM) is weakly (or strongly) nonnegative definite function matrix, the DHNNWFM will converge to a stable state in serial (or parallel) model, and if WFM consisted of strongly nonnegative definite function matrix and column (or row) diagonally dominant function matrix, DHNNWFM will converge to a stable state in parallel model.

New delay-range-dependent stability condition for fuzzy Hopfield neural networks via Wirtinger inequality

2020 ◽  
Vol 38 (5) ◽  
pp. 6099-6109
Author(s):  
Rupak Datta ◽  
Rajeeb Dey ◽  
Ramasamy Saravanakumar ◽  
Baby Bhattacharya ◽  
Tsung-Chih Lin
Keyword(s):  
Neural Networks ◽  
Stability Condition ◽  
Hopfield Neural Networks ◽  
Wirtinger Inequality

Stability Criterion of Discrete Hopfield Neural Networks with Weighted Function Matrix and one-Delay

2011 ◽  
Vol 271-273 ◽  
pp. 725-729
Author(s):  
Hai Yang Zou ◽  
Sha Sha Chen ◽  
Xiao Feng Lai
Keyword(s):  
Neural Networks ◽  
Stability Criterion ◽  
Hopfield Neural Networks ◽  
Definite Function ◽  
Weighted Function ◽  
Negative Definite Function ◽  
Parallel Mode ◽  
Diagonally Dominant ◽  
Serial Mode ◽  
Function Matrix

In this paper, discrete Hopfield neural networks with weight function matrix(WFM) and delay(DHNNWFMD) are presented. And we obtain an important result that if the WFM is a symmetric function matrix(SFM) and delayed function matrix(DFM) is a diagonally dominant function matrix(DDFM), DHNNWFMD will converge to a state in serial mode and if the WFM is a SFM and non-negative definite function matrix(NFM) and DFM is a DDFM, DHNNWFMD will converge to a state in parallel mode. It provides some theory basis for the application of DHNNWFMD.

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