[1]孙呈霞,吴向东,刘仙.一类耦合Jansen-Rit模型中突变参数的非线性辨识[J].燕山大学学报,2020,44(1):055-62.[doi:10.3969/j.issn.1007-791X.2020.01.008]
 SUN Chengxia,WU Xiangdong,LIU Xian.Nonlinear identification of mutation parameters in a class of coupled Jansen-Rit models[J].Journal of YanShan University,2020,44(1):055-62.[doi:10.3969/j.issn.1007-791X.2020.01.008]
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一类耦合Jansen-Rit模型中突变参数的非线性辨识
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《燕山大学学报》[ISSN:1007-791X/CN:13-1219/N]

卷:
44
期数:
2020年第1期
页码:
055-62
栏目:
计算机与控制工程
出版日期:
2020-01-31

文章信息/Info

Title:
Nonlinear identification of mutation parameters in a class of coupled Jansen-Rit models
文章编号:
1007-791X(2020)01-0055-08
作者:
孙呈霞1吴向东2刘仙 1*
1.燕山大学 电气工程学院,河北 秦皇岛 066004;2.北京理工大学 自动化学院,北京 100081
Author(s):
SUN Chengxia1WU Xiangdong2LIU Xian 1
1. School of Electrical Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China;
2. College of Automation,Beijing Institute of Technology,Beijing 100081,China

关键词:
强跟踪容积卡尔曼滤波器非线性辨识突变耦合Jansen-Rit模型
Keywords:
strong tracking cubature Kalman filternonlinear identificationmutationcoupled Jansen-Rit models
分类号:
Q332
DOI:
10.3969/j.issn.1007-791X.2020.01.008
文献标志码:
A
摘要:
耦合Jansen-Rit模型可以用于模拟脑电信号的产生,其中的兴奋性增益参数的变化对模型动力学特性有重要的影响,这种变化可以用于模拟异常脑电活动的产生,因而,辨识这类突变参数具有重要意义。针对一类具有突变兴奋性增益参数的耦合Jansen-Rit模型,本文给出一种基于强跟踪容积卡尔曼滤波器的新方法来解决非线性辨识和估计问题。强跟踪容积卡尔曼滤波器结合容积卡尔曼滤波器估计精度高和强跟踪滤波器跟踪性能强的优点,能够重构非线性系统中的兴趣变量,以及辨识系统中的突变参数。仿真验证了该方法的有效性。

Abstract:
The coupled Jansen-Rit model can be used to simulate the generation of electroencephalogram signals,in which the change of the excitatory parameter has an important influence on the dynamic characteristics of the model,and this change can be used to simulate the generation of abnormal EEG activities.Therefore,it is of great significance to identify the mutation parameters in the coupled Jansen-Rit model.For a class of coupled Jansen-Rit models with the mutation excitability parameter,a novel approach based on the strong tracking cubature Kalman filter is proposed to solve the nonlinear identifying and estimating problem.The strong tracking cubature Kalman filter combines the advantages of the highaccuracy of estimation of the cubature Kalman filter and the strong tracking ability of the strong tracking filter,which is able to reconstruct the interesting variables and identifies the mutation parameters of nonlinear systems.Simulation results confirm the effectiveness of the proposed method.

备注/Memo

备注/Memo:
收稿日期:2018-12-01       责任编辑:孙峰
基金项目:国家自然科学基金资助项目(61473245,61004050);河北省自然科学基金资助项目(F2017203218)
作者简介:孙呈霞 (1991-),女,河北秦皇岛人,博士研究生,主要研究方向为基于计算神经网络模型的脑节律调制;*通信作者:刘仙(1979-),女,浙江义乌人,博士,教授,博士生导师,主要研究方向为神经动力学系统的分析与控制,脑机接口,非线性系统分析与控制,Email:liuxian@ysu.edu.cn。

更新日期/Last Update: 2020-03-20