In this paper we consider simple neural network models consisting oftwo to three continuons nonlinear neurons, with no intrinsic synaptic plasticity, and with delay in neural signal transmission. We investigate thé différent dynamic régimes which may exist for thèse "minimal" neural network structures. Examples of stable, oscillatory (periodic or quasi-periodic), and chaotic régimes are reporte...

This paper proposes a simple methodology to construct an iterative neural network which mimics a given chaotic time series. The methodology uses the Gamma test to identify a suitable (possibly irregular) embedding of the chaotic time series from which a one step predictive model may be constructed. This model is then iterated to produce a close approximation to the original chaotic dynamics. Ha...

Abstract The Hopfield-Tank (1985) recurrent neural network architecture for the Traveling Salesman Problem is generalized to a fully interconnected “cellular” neural network of regular oscillators. Tours are defined by synchronization patterns, allowing the simultaneous representation of all cyclic permutations of a given tour. The network converges to local optima some of which correspond to s...

This research investigates the potential utility of chaotic dynamics in neural information processing. A novel chaotic spiking neural network model is presented which is composed of non-linear dynamic state (NDS) neurons. The activity of each NDS neuron is driven by a set of non-linear equations coupled with a threshold based spike output mechanism. If time-delayed self-connections are enabled ...

| We review the approaches for solving combinatorial optimization problems by chaotic dynamics. We mention both numerical algorithms with chaotic neural networks and hardware implementation. I. Chaos for avoiding local minima A. Mutual Connection Neural Network Dynamics Various methods are proposed for solving NP-hard combinatorial optimization problems, for example, traveling salesman problem ...

totoday’s market place is increasingly dynamic and volatile. in this area supply network issues recently have attracted a lot of attention from industrial practitioners and academics worldwide. chaos theory is the study of complex, nonlinear, dynamic systems. for chaotic systems, a tiny change in conditions may result in an enormous change in system output, whereas substantial changes in condit...

In this paper, a class of uncertain chaotic systems preceded by unknown backlash nonlinearity is investigated. Combining backstepping technique with fuzzy neural network identifying, an adaptive backstepping fuzzy neural controller (ABFNC) for uncertain chaotic systems with unknown backlash is proposed. The proposed ABFNC system is comprised of a fuzzy neural network identifier (FNNI) and a rob...

A functional model of limbic system of brain is proposed by combining four conventional models: a chaotic neural network (CNN), a multilayered chaotic neural network (MCNN), a hippocampus-neocortex model and an emotional model of amygdala. The composite model can realize mutual association of multiple time series patterns and transform short-term memory to long-term memory. The simulation resul...

Abstract. In this work, a neural networks is developed for modelling and controlling a chaotic system based on measured input-output data pairs. In the chaos modelling phase, a neural network is trained on the unknown system. Then, a predictive control mechanism has been implemented with the neural networks to reach the close neighborhood of the chosen unstable fixed point embedded in the chaot...