Results 291 to 300 of about 1,813,184 (347)

Anti-synchronization of complex-valued memristor-based delayed neural networks

Neural Networks, 2018
This paper investigates the anti-synchronization of complex-valued memristor-based neural networks with time delays via designed external controllers. By constructing appropriate Lyapunov functions and using inequality technique, two different types of ...
Dan Liu, Song Zhu, Kaili Sun
semanticscholar   +1 more source

Delay-Dependent Global Exponential Stability for Delayed Recurrent Neural Networks

IEEE Transactions on Neural Networks and Learning Systems, 2017
This paper deals with the global exponential stability for delayed recurrent neural networks (DRNNs). By constructing an augmented Lyapunov-Krasovskii functional and adopting the reciprocally convex combination approach and Wirtinger-based integral inequality, delay-dependent global exponential stability criteria are derived in terms of linear matrix ...
Yin Sheng, Yi Shen, Mingfu Zhu
openaire   +2 more sources

Design of memory controllers for finite-time stabilization of delayed neural networks with uncertainty

Journal of the Franklin Institute, 2018
In this paper, we investigate the problem of finite-time stabilization of time-varying delayed neural networks with uncertainty. By employing the Lyapunov approach and linear matrix inequalities (LMIs), two different memory controllers are derived to ...
Xiaoyu Zhang, Xiaodi Li, Jinde Cao, Foued Miaadi   +3 more
semanticscholar   +1 more source

Recurrent Neural Networks with Delays

1994
Recurrent neural networks (RNN) have been introduced for the modelization of non linear dynamic systems. In spite of their potential ability to implement systems of arbitrary complexity, they are often avoided because of large training times. For many problems, local recurrent networks or time delay feed forward architectures perform well enough.
J. Guignot, P. Gallinari
openaire   +1 more source

Improved Delay-Dependent Asymptotic Stability Criteria for Delayed Neural Networks

IEEE Transactions on Neural Networks, 2008
This brief is concerned with asymptotic stability of neural networks with uncertain delays. Two types of uncertain delays are considered: one is constant while the other is time varying. The discretized Lyapunov-Krasovskii functional (LKF) method is integrated with the technique of introducing the free-weighting matrix between the terms of the Leibniz ...
Chen, Wu-Hua, Zheng, Wei Xing (R8901)
openaire   +3 more sources

Delay-Derivative-Dependent Stability for Delayed Neural Networks With Unbound Distributed Delay

IEEE Transactions on Neural Networks, 2010
In this brief, based on Lyapunov-Krasovskii functional approach and appropriate integral inequality, a new sufficient condition is derived to guarantee the global stability for delayed neural networks with unbounded distributed delay, in which the improved delay-partitioning technique and general convex combination are employed. The LMI-based criterion
Tao, Li, Aiguo, Song, Shumin, Fei, Ting, Wang   +3 more
openaire   +2 more sources

Bifurcation Mechanism for Fractional-Order Three-Triangle Multi-delayed Neural Networks

Neural Processing Letters, 2022
Changjin Xu, Zixin Liu, Peiluan Li, Jinling Yan, Lingyun Yao   +4 more
semanticscholar   +1 more source

Fixed-time synchronization for coupled delayed neural networks with discontinuous or continuous activations

Neurocomputing, 2018
This paper is concerned with fixed-time synchronization of coupled delayed neural networks with discontinuous or continuous activation functions. Two discontinuous control protocols under undirected or directed topologies are proposed to guarantee that ...
Hui Lü, Wangli He, Q. Han, Chen Peng
semanticscholar   +1 more source

Trainable Delays in Time Delay Neural Networks for Learning Delayed Dynamics

IEEE Transactions on Neural Networks and Learning Systems
In this article, the connection between time delay systems and time delay neural networks (TDNNs) is presented from a continuous-time perspective. TDNNs are utilized to learn the nonlinear dynamics of time delay systems from trajectory data. The concept of TDNN with trainable delay (TrTDNN) is established, and training algorithms are constructed for ...
Xunbi A. Ji, Gábor Orosz
openaire   +2 more sources

On delayed impulsive Hopfield neural networks

Neural Networks, 1999
Many evolutionary processes, particularly some biological systems, exhibit impulsive dynamical behaviors, which can be well described by impulsive Hopfield neural networks. This paper formulates and studies a model of delayed impulsive Hopfield neural networks. Several fundamental issues such as global exponential stability, existence and uniqueness of
Zhi-Hong Guan, Guanrong Chen
openaire   +1 more source