Finite-time H∞ Filtering for Discrete-time Markovian Jump BAM Neural Networks with Time-varying Delays

2018 ◽  
Vol 16 (4) ◽  
pp. 1971-1980 ◽  
Author(s):  
M. Syed Ali ◽  
K. Meenakshi ◽  
Young Hoon Joo
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Finite Time ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Markovian Jump

Robust Finite-Time Passivity of Markovian Jump Discrete-Time BAM Neural Networks

2021 ◽  
pp. 341-371
Author(s):  
Grienggrai Rajchakit ◽  
Praveen Agarwal ◽  
Sriraman Ramalingam
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Finite Time ◽  
Bam Neural Networks ◽  
Markovian Jump

scholarly journals H_∞ Finite-time Boundedness for Discrete-time Delay Neural Networks via Reciprocally Convex Approach

2020 ◽  
Vol 36 (3) ◽  
Author(s):  
Le Anh Tuan
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Finite Time ◽  
Linear Matrix ◽  
Time Varying ◽  
Time Stability ◽  
Finite Time Stability ◽  
Discrete Time Delay ◽  
Finite Time Boundedness ◽  
Reciprocally Convex Approach

This paper addresses the problem of finite-time boundedness for discrete-time neural networks with interval-like time-varying delays. First, a delay-dependent finite-time boundedness criterion under the finite-time  performance index for the system is given based on constructing a set of adjusted Lyapunov–Krasovskii functionals and using reciprocally convex approach. Next, a sufficient condition is drawn directly which ensures the finite-time stability of the corresponding nominal system. Finally, numerical examples are provided to illustrate the validity and applicability of the presented conditions. Keywords: Discrete-time neural networks,  performance, finite-time stability, time-varying delay, linear matrix inequality.  

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