Abstract
A neural network is a model of the brain’s cognitive process, with a highly interconnected multiprocessor architecture. The neural network has incredible potential, in the view of these artificial neural networks inherently having good learning capabilities and the ability to learn different input features. Based on this, this paper proposes a new chaotic neuron model and a new chaotic neural network (CNN) model. It includes a linear matrix, a sine function, and a chaotic neural network composed of three chaotic neurons. One of the chaotic neurons is affected by the sine function. The network has rich chaotic dynamics and can produce multiscroll hidden chaotic attractors. This paper studied its dynamic behaviors, including bifurcation behavior, Lyapunov exponent, Poincaré surface of section, and basins of attraction. In the process of analyzing the bifurcation and the basins of attraction, it was found that the network demonstrated hidden bifurcation phenomena, and the relevant properties of the basins of attraction were obtained. Thereafter, a chaotic neural network was implemented by using FPGA, and the experiment proved that the theoretical analysis results and FPGA implementation were consistent with each other. Finally, an energy function was constructed to optimize the calculation based on the CNN in order to provide a new approach to solve the TSP problem.
Highlights
The human brain is the most complex and wonderful information processing organ
Complexity chaotic neurons. e study in [14] proposed a new fourdimensional chaotic neural memory cell neural network, studied its dynamic behaviors, and designed chaotic synchronization based on sliding mode control. e proposed chaotic memory CNN system can be used for secure communication. e study in [15] studied the construction of a blind restoration model for a superresolution image based on a chaotic neural network
We put forward a new chaotic neural network model and a resulting CNN model. e CNN has rich chaotic dynamic behaviors and can generate multiscroll hidden chaotic attractors. en, we study the dynamic behaviors, including bifurcation behaviors, Lyapunov exponent, Poincaresection, and the basins of attraction, and get knowledge of related characteristics of the basins of attraction
Summary
The human brain is the most complex and wonderful information processing organ. E study in [14] proposed a new fourdimensional chaotic neural memory cell neural network, studied its dynamic behaviors, and designed chaotic synchronization based on sliding mode control. E study in [15] studied the construction of a blind restoration model for a superresolution image based on a chaotic neural network. E study in [16] considered the circuit implementation and application of chaotic neural networks of reconfigurable memory. E study in [19] analyzed the sliding mode synchronization control of time-delayed chaotic neural networks based on the observer. E study in [21] considered the dynamic behaviors of chaotic circuits in neural networks. We simulated and studied a chaotic neural network consisting of a linear matrix, a sine function, and three chaotic neurons, one of which is affected by the sine function. Fewer researches have been conducted in the existing chaotic neural network research literature; the research on dynamics of this type of system is of paramount importance and meaningful
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