Terminal zeroing neural network for time-varying matrix computing under bounded noise

ZHONG Guomin; TANG Yifei; SUN Mingxuan

doi:10.11959/j.issn.1000-436x.2024166

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Terminal zeroing neural network for time-varying matrix computing under bounded noise

Papers | 更新时间：2024-10-10

- Terminal zeroing neural network for time-varying matrix computing under bounded noise
- Journal on Communications Vol. 45, Issue 9, Pages: 55-67(2024)
- 作者机构：
  
  浙江工业大学信息工程学院，浙江杭州 310023
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62073291;62222315);Zhejiang Provincial Natural Science Foundation of China(LZ22F030007)
- DOI：10.11959/j.issn.1000-436x.2024166
  CLC： TP183
- Received：11 March 2024，
  
  Revised：2024-08-27，
  
  Published：25 September 2024
- 稿件说明：
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仲国民,唐逸飞,孙明轩.用于有界噪声时变矩阵计算的终端零化神经网络[J].通信学报,2024,45(09):55-67.

ZHONG Guomin,TANG Yifei,SUN Mingxuan.Terminal zeroing neural network for time-varying matrix computing under bounded noise[J].Journal on Communications,2024,45(09):55-67.
仲国民,唐逸飞,孙明轩.用于有界噪声时变矩阵计算的终端零化神经网络[J].通信学报,2024,45(09):55-67. DOI： 10.11959/j.issn.1000-436x.2024166.

ZHONG Guomin,TANG Yifei,SUN Mingxuan.Terminal zeroing neural network for time-varying matrix computing under bounded noise[J].Journal on Communications,2024,45(09):55-67. DOI： 10.11959/j.issn.1000-436x.2024166.

摘要

为提升零化神经网络（ZNN）求解时变矩阵计算问题时的收敛性能，提出一种具有抗噪能力的终端零化神经网络（TZNN）及其对数加速形式（LA-TZNN）。对误差动态的终态吸引性展开分析，结果表明所提网络在受到有界噪声干扰时仍能在固定时间内使误差归零，其中LA-TZNN可实现对数调节时间稳定，收敛速度相较于TZNN更快。考虑到实际情况中初始误差有界，给出半全局意义上的调节时间上界，并通过设置可调参数，使网络实现预定义时间稳定。将2种模型应用于时变矩阵求逆和PUMA560机械臂重复运动规划问题，仿真结果验证了所提方法相较于传统ZNN设计，调节时间更短，收敛精度更高，并能够有效抑制有界噪声干扰。

Abstract

To improve the convergence performance of zeroing neural network (ZNN) for time-varying matrix computation problems solving

a terminal zeroing neural network (TZNN) with noise resistance and its logarithmically accelerated form (LA-TZNN) were proposed. The terminal attraction of the error dynamic equation were analyzed

and the results showed that the neural state of the proposed networks can converge to the theoretical solution within a fixed time when subjected to bounded noises. In addition

the LA-TZNN could achieve logarithmical settling-time stability

and its convergence speed was faster than the TZNN. Considering that the initial error was bounded in actual situations

an upper bound of the settling-time in a semi-global sense was given

and an adjustable parameter was set to enable the network to converge within a predefined time. The two proposed models were applied to solve the time-varying matrix inversion and trajectory planning of redundant manipulators PUMA560. The simulation results further verified that compared with the conventional ZNN design

the proposed methods have shorter settling-time

higher convergence accuracy

and can effectively suppress bounded noise interference.

关键词

Keywords

references

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