[1]毛华倩,孔颖.面向时变复数的模糊归零神经网络算法[J].浙江科技大学学报,2024,(01):49-58.[doi:10.3969/j.issn.1671-8798.2024.01.006 ]
 MAO Huaqian,KONG Ying.On fuzzy zeroing neural network algorithm for computing time-varying complex numbers[J].,2024,(01):49-58.[doi:10.3969/j.issn.1671-8798.2024.01.006 ]
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面向时变复数的模糊归零神经网络算法(/HTML)
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《浙江科技大学学报》[ISSN:2097-5236/CN:33-1431/Z]

卷:
期数:
2024年01期
页码:
49-58
栏目:
出版日期:
2024-02-29

文章信息/Info

Title:
On fuzzy zeroing neural network algorithm for computing time-varying complex numbers
文章编号:
1671-8798(2024)01-0049-10
作者:
毛华倩孔颖
(浙江科技大学 信息与电子工程学院,杭州 310023)
Author(s):
MAO Huaqian KONG Ying
(School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, Zhejiang, China)
关键词:
复数归零神经网络 模糊逻辑系统 有限时间收敛 激活函数
分类号:
TP183
DOI:
10.3969/j.issn.1671-8798.2024.01.006
文献标志码:
A
摘要:
【目的】为了求解时变复数西尔维斯特方程(Sylvester equation),提出两种全新的复值模糊归零神经网络(fuzzy logic system for zeroing neural network,FLSVZNN)模型。【方法】首先,在两种复数归零神经网络(complex-valued zeroing neural network,CVZNN)模型的基础上,引入模糊逻辑系统(fuzzy logic system,FLS)来控制信号的处理,从而提出两种FLSVZNN模型; 然后,利用李亚普诺夫定理(Lyapunov's theorem)来分析模型的稳定性和收敛速度; 最后,通过仿真试验来进一步验证FLSVZNN的优越性能。【结果】在求解时变复数西尔维斯特方程时,相比传统的神经网络模型,使用改进的符号双幂(sign-bi-power,SBP)函数来激活的FLSVZNN模型具有更好的收敛性和稳定性,可使误差函数在0.3 s左右收敛至0。【结论】本研究提出的两种FLSVZNN模型能快速求解时变复数西尔维斯特方程,这可为神经网络模型的建立及工程应用提供参考。

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2023-04-23
基金项目:浙江省自然科学基金项目(LZY22E050002)
通信作者:孔 颖(1980— ),女,浙江省杭州人,教授,博士,主要从事神经网络与机械臂轨迹规划研究。Email:kongying-888@163.com。
更新日期/Last Update: 2024-02-29