首页|基于粒子群算法的模糊大脑情感学习非线性系统辨识

基于粒子群算法的模糊大脑情感学习非线性系统辨识

Nonlinear system identification based on fuzzy brain emotional learning with particle swarm algorithm

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为提高神经网络模型在解决非线性系统辨识问题上的精度,提出一种基于粒子群算法的模糊大脑情感学习模型.该模型包含大脑情感学习网络,在利用系统历史数据对模型进行训练的基础上,通过适应度函数动态调整网络结构中的权重因子,提高网络学习效率和辨识精度.在对连续搅拌反应器系统辨识试验和对sin E强非线性对象逼近试验中,与常规模糊大脑情感学习模型、BP神经网络和RBF神经网络相比,本模型拥有更高的逼近能力和更快的收敛速度,解决了基于试错法导致的模型参数调整时间长、模型不稳定等问题,为辨识的实际应用提供了可行的模型选择.
A fuzzy brain emotional learning model based on particle swarm algorithm was proposed to improve the accuracy of neu-ral network model in solving nonlinear system recognition problems.The model contained a brain emotional learning network,and based on the training of the model using the system historical data,the adaptation function made the dynamic adjustment of the weight factors in the network structure to improve the network learning efficiency and recognition accuracy.In the identification test of continuous stirred tank reactor and the approximation test of sinE strong nonlinear object,compared with the conventional fuzzy brain emotional learning model,BP neural network and RBF neural network,this model had higher approximation ability and faster convergence speed,and solved the problems of long adjustment time of model parameters and model instability caused by trial-and-error based method,which provided a feasible model for practical application of recognition.

particle swarm algorithmbrain-like neural networkfuzzy brainemotional learning modelneural network system iden-tificationnonlinear system

孙园、曾惠权、欧阳苏建、高佳倩、王绮楠、林智勇

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厦门理工学院电气工程与自动化学院, 福建 厦门 361024

厦门市高端电力装备及智能控制重点实验室, 福建 厦门 361024

粒子群算法 类脑神经网络 大脑模糊情感学习模型 神经网络系统辨识 非线性系统

福建省自然科学基金资助项目厦门市自然科学基金资助项目厦门理工学院高层次人才科研启动项目厦门理工学院研究生科技创新项目

2020J012813502Z20227215YKJ22060RYKJCX2021128

2024

10.6040/j.issn.1672-3961.0.2022.383

山东大学学报(工学版)
山东大学

山东大学学报(工学版)

CSTPCD北大核心
影响因子：0.634
ISSN：1672-3961
年,卷(期)：2024.54(1)
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