New construction method of bidirectional associative memory for object recognition

1996 ◽  
Author(s):  
Te-Shin Chen ◽  
Kang-Ping Lin ◽  
Being-Tau Chung
Keyword(s):  
Object Recognition ◽  
Associative Memory ◽  
Construction Method ◽  
Bidirectional Associative Memory ◽  
New Construction

A Reconfigurable Bidirectional Associative Memory Network with Memristor Bridge

2021 ◽  
Author(s):  
Yingying Li ◽  
Junrui Li ◽  
Jie Li ◽  
Shukai Duan ◽  
Lidan Wang ◽  
...  
Keyword(s):  
Associative Memory ◽  
Bidirectional Associative Memory ◽  
Memory Network

Numerical Analysis of Metro Station Construction by Large-Diameter Shield and Pile-Beam-Arch Method

2011 ◽  
Vol 368-373 ◽  
pp. 2711-2715 ◽  
Author(s):  
De Yun Ding ◽  
Xiu Ren Yang ◽  
Wei Dong Lu ◽  
Wei Ning Liu ◽  
Mei Yan ◽  
...  
Keyword(s):  
Ground Deformation ◽  
Large Diameter ◽  
Element Model ◽  
Construction Method ◽  
Shield Tunnel ◽  
Outer Diameter ◽  
Shield Tunneling ◽  
Metro Station ◽  
Dimensional Finite Element ◽  
New Construction

In more and more complicated urban building environment, a new construction method that metro engineering is constructed by large-diameter shield and shallow mining method can be regarded as a great attempt in China. By taking the Gaojiayuan station of Beijing metro line 14 as an engineering background, the main construction steps for the platform of the metro station built by a large-size shield with an outer diameter of 10 m and the Pile-Beam-Arch (PBA) method are introduced. Based on the soil-structure interaction theory, a two-dimensional finite element model is used to simulate the shield tunneling and the platform construction by the PBA method to enlarge the shield tunnel. The ground deformation and structural stress of the platform are predicted. The numerical results can be regarded as a valuable reference for the application of the new construction method in Beijing metro line 14.

Robust stability analysis for delayed Cohen-Grossberg-type bidirectional associative memory neural networks with norm-bounded uncertainties

2009 ◽  
Vol 223 (5) ◽  
pp. 693-707
Author(s):  
Y Wang ◽  
P Hu
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Robust Stability ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Bidirectional Associative Memory ◽  
Parameter Uncertainties ◽  
Robust Stability Analysis ◽  
Global Robust Stability ◽  
Bounded Uncertainties

In this paper, the problem of global robust stability is discussed for uncertain Cohen-Grossberg-type (CG-type) bidirectional associative memory (BAM) neural networks (NNs) with delays. The parameter uncertainties are supposed to be norm bounded. The sufficient conditions for global robust stability are derived by employing a Lyapunov-Krasovskii functional. Based on these, the conditions ensuring global asymptotic stability without parameter uncertainties are established. All conditions are expressed in terms of linear matrix inequalities (LMIs). In addition, two examples are provided to illustrate the effectiveness of the results obtained.

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