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Particle swarm algorithm-based neuron swarm model parameter adaptive optimization method

A particle swarm algorithm and optimization method technology, applied in the field of nervous system, can solve rare and other problems

Active Publication Date: 2019-11-08
YANSHAN UNIV
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Problems solved by technology

However, the application of particle swarm optimization in the adaptive optimization of neuron swarm model parameters is still rare.

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  • Particle swarm algorithm-based neuron swarm model parameter adaptive optimization method
  • Particle swarm algorithm-based neuron swarm model parameter adaptive optimization method
  • Particle swarm algorithm-based neuron swarm model parameter adaptive optimization method

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Embodiment Construction

[0042] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0043] EEG includes 5 frequency bands δ, θ, a, low β 、high β and γ, where, low β It is the slow wave of β frequency band, high β For the fast wave in the β frequency band, δ, θ, a, low β 、high β and the target frequency f of γ g Different, in the present embodiment, the target frequency f of the 5 frequency bands of EEG g Set respectively: δ=3Hz, θ=5Hz, α=12Hz, low β = 17Hz, high β =25Hz, γ=41Hz, respectively perform steps S1-S6 for the five frequency bands of the EEG, so as to obtain the optimal parameter combination of each frequency band.

[0044] Such as figure 1 Shown is the flowchart of the neuron swarm model parameter adaptive optimization method based on the particle swarm algorithm of the present invention, comprising the following steps:

[0045] Step S1: Initialize the particle swarm, set the basic parameters of the particl...

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Abstract

The invention discloses a particle swarm algorithm-based neuron swarm model parameter adaptive optimization method. The method comprises the steps of S1, initializing a particle swarm, setting basic parameters of the particle swarm algorithm, setting a parameter combination search range of each particle, and setting basic parameters of a neuron swarm model; S2, calculating a fitness value of eachparticle in the initial particle swarm, and initializing a particle individual extremum and a global extremum according to the fitness value of the particle; S3, updating the particle speed and position; s4, calculating a new particle fitness value, and updating a particle individual extreme value and a global extreme value; s5, judging whether the maximum number of iterations is met or not, if yes, outputting globally optimal particles, and otherwise, returning to the step S3; and S6, obtaining an optimal parameter combination of the electroencephalogram frequency band according to the globally optimal particles output in the step S5. The invention provides a convenient and efficient neuron group model parameter adjustment method, the parameter identification accuracy is improved, and theadjustment time is shortened.

Description

technical field [0001] The invention relates to the field of nervous systems, in particular to an adaptive optimization method for neuron group model parameters based on particle swarm algorithm. Background technique [0002] With the development of nervous system simulation modeling technology, the establishment of neuron models of EEG signals has become an important way to study the generation, transmission and processing of EEG signals. EEG signal neural models can be divided into two categories, one is the computational neural model at the micro level, which is difficult to determine the parameters of the neuron model and consumes a lot of computing resources; the other is the lumped parameter model, such as The neuron group model (neural mass model, NMM), which models the overall characteristics of the neuron group composed of specific types of cells at the macro level, has a simple mathematical form and can better reflect the meaning of neurophysiology , so it is wide...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06N3/00
CPCG06N3/08G06N3/006G06N3/048G06N3/044G06N3/045
Inventor 谢平袁航陈晓玲张昌梦金子强程生翠张园园
Owner YANSHAN UNIV
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