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A chaotic neural network (CNN) composed of chaotic neurons exhibits chaotic associative dynamics for some values of the parameters, which indicate that the CNN is a promising technique that can be applied to information processing such as pattern recognition and memory recalling. However, the outputs of the CNN in those parameter areas wander around all the(More)
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(More)
In the paper, the modified fuzzy c-means (MFc) is firstly used to treat the ill-balanced fMRI dataset to improve the efficiency, remove the redundance and reduce the population of analyzed voxels. Then the iteration self-organization data analysis techniques algorithm (ISODATA) method, as the development of data-driving methods, is utilized to find out the(More)
The chaotic neural network is studied under the threshold activated coupling, which provides a controlled output patterns. In general the chaotic neural network constructed with chaotic neurons exhibits very rich dynamic behaviors with a nonperiodic associative memory. In the chaotic neural network, it is difficult to distinguish the stored patterns from(More)
In this paper, we proposed an improved delay feedback control method (IDFC) for a chaotic neural network. In the method, a delay feedback control signal is added into the term of the refractoriness of the chaotic neuron to resist the chaos in the chaotic neural network. The computer experiments show that the output sequence of the controlled chaotic neural(More)
In this study, a method is proposed that eliminates spiral waves in a locally connected chaotic neural network (CNN) under some simplified conditions, using a dynamic phase space constraint (DPSC) as a control method. In this method, a control signal is constructed from the feedback internal states of the neurons to detect phase singularities based on their(More)
Brain waves are classified as gamma, beta, alpha, theta, and delta waves to quantify brain activity and can be approximated as sinusoidal waves of different frequencies. In this work, we use sinusoidal waves at two different frequencies to control chaos in a chaotic neural network (CNN) to explore the effect of multi-frequency sinusoidal waves in chaos(More)
We have studied the chaos control in chaotic neural network by limiting the phase space at varying time interval. It provides a controlled output patterns with different temporal periods depending upon the control parameters. The chaotic neural network constructed with chaotic neurons exhibits very rich dynamic behavior with a non–periodic associative(More)