8,243 research outputs found
Controlling chaos in a chaotic neural network
The chaotic neural network constructed with chaotic neuron shows the associative memory function, but its memory searching process cannot be stabilized in a stored state because of the chaotic motion of the network. In this paper, a pinning control method focused on the chaotic neural network is proposed. The computer simulation proves that the chaos in the chaotic neural network can be controlled with this method and the states of the network can converge in one of its stored patterns if the control strength and the pinning density are chosen suitable. It is found that in general the threshold of the control strength of a controlled network is smaller at higher pinned density and the chaos of the chaotic neural network can be controlled more easily if the pinning control is added to the variant neurons between the initial pattern and the target pattern
Neural Predictive Control of Unknown Chaotic Systems
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 chaotic systems. Effectiveness of the proposed method for both modelling and prediction-based control on the chaotic logistic equation and HĂ©non map has been demonstrated
Controlling Chaos in a Neural Network Based on the Phase Space Constraint
The chaotic neural network constructed with chaotic neurons exhibits very rich dynamic
behaviors and has a nonperiodic associative memory. In the chaotic neural network,
however, it is dicult to distinguish the stored patters from others, because the states of
output of the network are in chaos. In order to apply the nonperiodic associative memory
into information search and pattern identication, etc, it is necessary to control chaos in
this chaotic neural network. In this paper, the phase space constraint method focused on
the chaotic neural network is proposed. By analyzing the orbital of the network in phase
space, we chose a part of states to be disturbed. In this way, the evolutional spaces of
the strange attractors are constrained. The computer simulation proves that the chaos
in the chaotic neural network can be controlled with above method and the network can
converge in one of its stored patterns or their reverses which has the smallest Hamming
distance with the initial state of the network. The work claries the application prospect
of the associative dynamics of the chaotic neural network
Modelling and control of chaotic processes through their Bifurcation Diagrams generated with the help of Recurrent Neural Network models: Part 1âsimulation studies
Many real-world processes tend to be chaotic and also do not lead to satisfactory analytical modelling. It has been shown here that for such chaotic processes represented through short chaotic noisy time-series, a multi-input and multi-output recurrent neural networks model can be built which is capable of capturing the process trends and predicting the future values from any given starting condition. It is further shown that this capability can be achieved by the Recurrent Neural Network model when it is trained to very low value of mean squared error. Such a model can then be used for constructing the Bifurcation Diagram of the process leading to determination of desirable operating conditions. Further, this multi-input and multi-output model makes the process accessible for control using open-loop/closed-loop approaches or bifurcation control etc. All these studies have been carried out using a low dimensional discrete chaotic system of HĂ©non Map as a representative of some real-world processes
Controlling chaos in diluted networks with continuous neurons
Diluted neural networks with continuous neurons and nonmonotonic transfer
function are studied, with both fixed and dynamic synapses. A noisy stimulus
with periodic variance results in a mechanism for controlling chaos in neural
systems with fixed synapses: a proper amount of external perturbation forces
the system to behave periodically with the same period as the stimulus.Comment: 11 pages, 8 figure
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