A comprehensive review of stability analysis of continuous-time recurrent neural networks
Stability problems of continuous-time recurrent neural networks have been extensively
studied, and many papers have been published in the literature. The purpose of this paper is …
studied, and many papers have been published in the literature. The purpose of this paper is …
An overview of recent developments in Lyapunov–Krasovskii functionals and stability criteria for recurrent neural networks with time-varying delays
Global asymptotic stability is an important issue for wide applications of recurrent neural
networks with time-varying delays. The Lyapunov–Krasovskii functional method is a …
networks with time-varying delays. The Lyapunov–Krasovskii functional method is a …
[BOOK][B] Neural networks: an introduction
B Müller, J Reinhardt, MT Strickland - 2012 - books.google.com
Neural Networks presents concepts of neural-network models and techniques of parallel
distributed processing in a three-step approach:-A brief overview of the neural structure of …
distributed processing in a three-step approach:-A brief overview of the neural structure of …
Dissecting neural odes
Continuous deep learning architectures have recently re-emerged as Neural Ordinary
Differential Equations (Neural ODEs). This infinite-depth approach theoretically bridges the …
Differential Equations (Neural ODEs). This infinite-depth approach theoretically bridges the …
A new fixed-time stability theorem and its application to the fixed-time synchronization of neural networks
In this paper, we derive a new fixed-time stability theorem based on definite integral,
variable substitution and some inequality techniques. The fixed-time stability criterion and …
variable substitution and some inequality techniques. The fixed-time stability criterion and …
[BOOK][B] Elegant chaos: algebraically simple chaotic flows
JC Sprott - 2010 - books.google.com
This heavily illustrated book collects in one source most of the mathematically simple
systems of differential equations whose solutions are chaotic. It includes the historically …
systems of differential equations whose solutions are chaotic. It includes the historically …
Neural networks as spatio-temporal pattern-forming systems
B Ermentrout - Reports on progress in physics, 1998 - iopscience.iop.org
Abstract Models of neural networks are developed from a biological point of view. Small
networks are analysed using techniques from dynamical systems. The behaviour of spatially …
networks are analysed using techniques from dynamical systems. The behaviour of spatially …
Adaptive synchronization of reaction–diffusion neural networks and its application to secure communication
This paper is mainly concerned with the synchronization problem of reaction-diffusion neural
networks (RDNNs) with delays and its direct application in image secure communications …
networks (RDNNs) with delays and its direct application in image secure communications …
Delay-dependent stability analysis of neural networks with time-varying delay: A generalized free-weighting-matrix approach
This paper investigates the delay-dependent stability problem of continuous neural networks
with a bounded time-varying delay via Lyapunov–Krasovskii functional (LKF) method. This …
with a bounded time-varying delay via Lyapunov–Krasovskii functional (LKF) method. This …
Stability analysis of discrete-time neural networks with time-varying delay via an extended reciprocally convex matrix inequality
This paper is concerned with the stability analysis of discrete-time neural networks with a
time-varying delay. Assessment of the effect of time delays on system stability requires …
time-varying delay. Assessment of the effect of time delays on system stability requires …