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This paper introduces a chaotic encryption system using a principal component analysis (PCA) neural network. The PCA neural network can produce the chaotic behaviors under certain conditions so that it serves as a pseudo-random number generator to generate random private keys. In this encryption system, the one-time pad encryption method is used, which is(More)
A winner-take-all Lotka–Volterra recurrent neural network with N × N neurons is proposed in this paper. Sufficient conditions for existence of winner-take-all stable equilibrium points in the network are obtained. These conditions guarantee that there is one and only one winner in each row and each column at any stable equilibrium point. In addition,(More)
It is well known that contours in image are salient when a series of edge elements are aligned in a collinear or co-circular fashion. In this paper, a contour extractor is constructed to extract contours by using the competitive layer mode implemented by Lotka-Volterra recurrent neural networks. This extractor can bind all edge elements which belong to a(More)
The competitive layer model (CLM) implemented by the Lotka–Volterra recurrent neural networks (LV RNNs) is prominently characterized by its capability of binding neurons with similar feature into the same layer by competing among neurons at different layers in a column. This paper proposes to use the CLM of the LV RNN for detecting brain activated regions(More)
This paper proposes to study the activity invariant sets and exponentially stable attractors of Lotka-Volterra recurrent neural networks. The concept of activity invariant sets deeply describes the property of an invariant set by that the activity of some neurons keeps invariant all the time. Conditions are obtained for locating activity invariant sets.(More)
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