Abstract
Biological neural network system is a complex nonlinear dynamic system, and research on its dynamics is an important topic at home and abroad. This paper briefly introduces the dynamic characteristics and influencing factors of the neural network system, including the effects of time delay and noise on neural network synchronization, synchronous transition, and stochastic resonance, and introduces the modeling of the neural network system. There are irregular mixing problems in the complex biological neural network system. The BP neural network algorithm can be used to solve more complex dynamic behaviors and can optimize the global search. In order to ensure that the neural network increases the biological characteristics, this paper adjusts the parameters of the BP neural network to receive EEG signals in different states. It can simulate different frequencies and types of brain waves, and it can also carry out a variety of simulations during the operation of the system. Finally, the experimental analysis shows that the complex biological neural network model proposed in this paper has good dynamic characteristics, and the application of this algorithm to data information processing, data encryption, and many other aspects has a bright prospect.
Highlights
Artificial neural network model, as a system of effective data processing, is a nonlinear dynamic system
Because artificial neural networks have powerful data information processing and analysis capabilities, domestic researchers have gradually begun to define artificial neural network algorithms for simulation. e multichannel memory resistance pulse-coupled neural network model proposed based on nano-level memory resistance accurately simulates the neural network. e change of the middle connection coefficient can effectively solve the estimation problem of the corresponding parameter in the neural network algorithm process
A 3D memristive HR neuron model based on the overall hidden oscillation is proposed, which can truly reflect the complex dynamic characteristics of brain waves in the neural network [4]
Summary
Biological neural network system is a complex nonlinear dynamic system, and research on its dynamics is an important topic at home and abroad. is paper briefly introduces the dynamic characteristics and influencing factors of the neural network system, including the effects of time delay and noise on neural network synchronization, synchronous transition, and stochastic resonance, and introduces the modeling of the neural network system. ere are irregular mixing problems in the complex biological neural network system. e BP neural network algorithm can be used to solve more complex dynamic behaviors and can optimize the global search. In order to ensure that the neural network increases the biological characteristics, this paper adjusts the parameters of the BP neural network to receive EEG signals in different states. It can simulate different frequencies and types of brain waves, and it can carry out a variety of simulations during the operation of the system. The experimental analysis shows that the complex biological neural network model proposed in this paper has good dynamic characteristics, and the application of this algorithm to data information processing, data encryption, and many other aspects has a bright prospect
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