用于有界噪声时变矩阵计算的终端零化神经网络
Terminal zeroing neural network for time-varying matrix computing under bounded noise
仲国民 1唐逸飞 1孙明轩1
作者信息
- 1. 浙江工业大学信息工程学院,浙江 杭州 310023
- 折叠
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 computa-tion problems solving,a terminal zeroing neural network(TZNN)with noise resistance and its logarithmically acceler-ated form(LA-TZNN)were proposed.The terminal attraction of the error dynamic equation were analyzed,and the re-sults 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 up-per bound of the settling-time in a semi-global sense was given,and an adjustable parameter was set to enable the net-work to converge within a predefined time.The two proposed models were applied to solve the time-varying matrix in-version and trajectory planning of redundant manipulators PUMA560.The simulation results further verified that com-pared 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
时变矩阵计算/零化神经网络/固定/预定义时间收敛/重复运动规划Key words
time-varying matrix computation/ZNN/fixed/predefined-time convergence/repetitive motion planning引用本文复制引用
基金项目
国家自然科学基金资助项目(62073291)
国家自然科学基金资助项目(62222315)
浙江省自然科学基金资助项目(LZ22F030007)
出版年
2024
国家科技期刊平台
NSTL
万方数据