Stability and bifurcation analysis of a six-neuron BAM neural network model with discrete delays

In this paper, a six-neuron BAM neural network model with discrete delays is considered. By analyzing the associated characteristic transcendental equation, the linear stability of the model is investigated and Hopf bifurcation is demonstrated. Some explicit formulae determining the stability and the direction of the Hopf bifurcation periodic solutions bifurcating from Hopf bifurcations are obtained by using the normal form method and center manifold theory. Finally, numerical simulations supporting the theoretical analysis are given.     

Synchronization and synchronized periodic solution in a simplified five-neuron BAM neural network with delays

A delay-differential equation, modeling a bidirectional associative memory (BAM) neural network with five neurons, is considered. Some results of synchronization and bifurcation are exhibited. By Lyapunov functional methods, some sufficient conditions for the absolute synchronization of the system and global attractivity of the trivial solution are established. This synchronization is independent of the size of time delay. Furthermore, delay-induced synchronized periodic solution is given analytically, as well as necessary and sufficient conditions for the synchronized periodic solution by perturbation-incremental scheme (PIS). The main difference in this paper from previous works in the literatures is that delay-induced synchronization is studied quantitatively. Theoretical results are illustrated with numerical simulations.     

Global existence of periodic solutions in a six-neuron BAM neural network model with discrete delays

In this paper, a six-neuron BAM neural network model with discrete delays is considered. Using the global Hopf bifurcation theorem for FDE due to Wu [Symmetric functional differential equations and neural networks with memory, Trans. Am. Math. Soc. 350 (1998) 4799-4838] and the Bendixson's criterion for high-dimensional ODE due to Li and Muldowney [On Bendixson' criterion, J. Differential Equations 106 (1994) 27-39], a set of sufficient conditions for the system to have multiple periodic solutions are derived when the sum of delays is sufficiently large.     

Robust stability of interval bidirectional associative memory neural network with time delays.

In this paper, the conventional bidirectional associative memory (BAM) neural network with signal transmission delay is intervalized in order to study the bounded effect of deviations in network parameters and external perturbations. The resultant model is referred to as a novel interval dynamic BAM (IDBAM) model. By combining a number of different Lyapunov functionals with the Razumikhin technique, some sufficient conditions for the existence of unique equilibrium and robust stability are derived. These results are fairly general and can be verified easily. To go further, we extend our investigation to the time-varying delay case. Some robust stability criteria for BAM with perturbations of time-varying delays are derived. Besides, our approach for the analysis allows us to consider several different types of activation functions, including piecewise linear sigmoids with bounded activations as well as the usual C1-smooth sigmoids. We believe that the results obtained have leading significance in the design and application of BAM neural networks.     

Sensitivity to noise in bidirectional associative memory (BAM)

Original Hebbian encoding scheme of bidirectional associative memory (BAM) provides a poor pattern capacity and recall performance. Based on Rosenblatt's perceptron learning algorithm, the pattern capacity of BAM is enlarged, and perfect recall of all training pattern pairs is guaranteed. However, these methods put their emphases on pattern capacity, rather than error correction capability which is another critical point of BAM. This paper analyzes the sensitivity to noise in BAM and obtains an interesting idea to improve noise immunity of BAM. Some researchers have found that the noise sensitivity of BAM relates to the minimum absolute value of net inputs (MAV). However, in this paper, the analysis on failure association shows that it is related not only to MAV but also to the variance of weights associated with synapse connections. In fact, it is a positive monotone increasing function of the quotient of MAV divided by the variance of weights. This idea provides an useful principle of improving error correction capability of BAM. Some revised encoding schemes, such as small variance learning for BAM (SVBAM), evolutionary pseudorelaxation learning for BAM (EPRLAB) and evolutionary bidirectional learning (EBL), have been introduced to illustrate the performance of this principle. All these methods perform better than their original versions in noise immunity. Moreover, these methods have no negative effect on the pattern capacity of BAM. The convergence of these methods is also discussed in this paper. If there exist solutions, EPRLAB and EBL always converge to a global optimal solution in the senses of both pattern capacity and noise immunity. However, the convergence of SVBAM may be affected by a preset function.     

Delay-induced bifurcation in a tri-neuron fractional neural network

This paper investigates the issue of stability and bifurcation for a delayed fractional neural network with three neurons by applying the sum of time delays as the bifurcation parameter. Based on fractional Laplace transform and the method of stability switches, some explicit conditions for describing the stability interval and emergence of Hopf bifurcation are derived. The analysis indicates that time delay can effectively enhance the stability of fractional neural networks. In addition, it is found that the stability interval can be varied by regulating the fractional order if all the parameters are fixed including time delay. Finally, numerical examples are presented to validate the derived theoretical results.     

Delay-induced Hopf bifurcation in a noise-driven excitable neuron model

A FitzHugh–Nagumo neuron model with cubic nonlinearity and discrete delay is considered, in which the time delay is regarded as a parameter. The effect of time delay on the linear stability and Hopf bifurcation of the model is studied. The existence, stability and direction of the local and global Hopf bifurcation are derived. Some numerical simulations are employed to validate the main results of this work.     

Stability and bifurcation of genetic regulatory networks with small RNAs and multiple delays

In this paper, we study the stability, bifurcation and periodic oscillation of two-gene regulatory networks mediated by small RNAs (sRNAs) with multiple delays. We first show the effect of sRNAs on the stability and bifurcation of genetic regulatory networks. Then we present sufficient conditions for the local stability of two-gene genetic regulatory networks in the parameter space, and assess critical values of the Hopf bifurcation. Although such networks may have multiple delays, sRNAs, positive feedbacks and different connection strengths among two genes, their stability and bifurcation depend on the sum of all time delays among all elements (including both mRNAs and proteins). Furthermore, the period of oscillations increases with the time delay, and in the case of larger delay, the amplitude of oscillations is robust against the change in the delay. Two examples are employed to illustrate the theorems developed in this study.     

Hopf bifurcation and oscillations in a communication network with heterogeneous delays

In this paper, we investigate stability, bifurcation and oscillations arising in a single-link communication network model with a large number of heterogeneous users adopting a Transmission Control Protocol (TCP)-like rate control scheme with an Active Queue Management (AQM) router. In the system considered, different user delays are known and fixed but taken from a given distribution. It is shown that for any given distribution of delays, there exists a critical amount of feedback (due to AQM) at which the equilibrium loses stability and a limit cycling solution develops via a Hopf bifurcation. The nature (criticality) of the bifurcation is investigated with the aid of Lyapunov-Schmidt perturbation method. The results of the analysis are numerically verified and provide valuable insights into dynamics of the AQM control system.