Equilibria and their bifurcations in a recurrent neural network involving iterates of a transcendental function

Analysis of Equilibria for a Class of Recurrent Neural Networks With Two Subnetworks

This article is concerned with the problem of the number and dynamical properties of equilibria for a class of connected recurrent networks with two switching subnetworks. In this network model, parameters serve as switches that allow two subnetworks to be turned ON or OFF among different dynamic states. The two subnetworks are described by a nonlinear coupled equation with a complicated relation among network parameters. Thus, the number and dynamical properties of equilibria have been very hard to investigate. By using Sturm's theorem, together with the geometrical properties of the network equation, we give a complete analysis of equilibria, including the existence, number, and dynamical properties. Necessary and sufficient conditions for the existence and exact number of equilibria are established. Moreover, the dynamical property of each equilibrium point is discussed without prior assumption of their locations. Finally, simulation examples are given to illustrate the theoretical results in this article.

Undamped Oscillations Generated by Hopf Bifurcations in Fractional-Order Recurrent Neural Networks With Caputo Derivative

In this paper, a fractional-order recurrent neural network is proposed and several topics related to the dynamics of such a network are investigated, such as the stability, Hopf bifurcations, and undamped oscillations. The stability domain of the trivial steady state is completely characterized with respect to network parameters and orders of the commensurate-order neural network. Based on the stability analysis, the critical values of the fractional order are identified, where Hopf bifurcations occur and a family of oscillations bifurcate from the trivial steady state. Then, the parametric range of undamped oscillations is also estimated and the frequency and amplitude of oscillations are determined analytically and numerically for such commensurate-order networks. Meanwhile, it is shown that the incommensurate-order neural network can also exhibit a Hopf bifurcation as the network parameter passes through a critical value which can be determined exactly. The frequency and amplitude of bifurcated oscillations are determined.

An oscillatory criterion for a time delayed neural ring network model

The effects of delays on dynamical networks and the stability analysis of time delayed systems have received a notable attention over the past decades. In this paper, the effects of delays on the oscillatory properties of a neural ring networks model are considered. The existence of oscillations for a specific type of recurrent neural network with time delays between neural interconnections is investigated. By using Chafee's closed orbit theory, some sufficient conditions for permanent oscillations are obtained. Simple and practical criteria for selecting the range of parameters in this network model are also derived. Among other things, the solutions that we provide can be applied to various activation functions. A few computer simulations are presented to support our analysis. The present study can be applied to analyze under which conditions a ring network could be exploited as an oscillatory pattern generator.

On some necessary and sufficient conditions for a recurrent neural network model with time delays to generate oscillations

In this paper, the existence of oscillations for a class of recurrent neural networks with time delays between neural interconnections is investigated. By using the fixed point theory and Liapunov functional, we prove that a recurrent neural network might have a unique equilibrium point which is unstable. This particular type of instability, combined with the boundedness of the solutions of the system, will force the network to generate a permanent oscillation. Some necessary and sufficient conditions for these oscillations are obtained. Simple and practical criteria for fixing the range of parameters in this network are also derived. Typical simulation examples are presented.