摘要
为解决压电陶瓷迟滞建模问题,提出一种新型的G-S混沌神经网络模型.该网络由输入层、隐层和输出层构成,在输入层中引入延迟环节,从而使得历史输入能够对当前输入的响应产生影响.网络的学习过程是一种混沌优化算法,可有效避免普通神经网络的局部极值和假饱和现象的发生.将该网络应用于纳米定位系统压电陶瓷执行器迟滞建模中,可以降低建模误差,实验结果验证了该方法的有效性.
A novel G-S chaotic neural network is proposed to resolve the hysteresis model of piezoceramics. The network has three layers： input layer, hidden layer and output layer. The input layer comprises the delay link, which maks the historical input capable to affect the current response. The learning algorithm is a process of chaos optimization, which can make the network avoid the local minima problem and false saturation phenomenon. The network can reduce the modeling error for the piezoelectric actuator of a nanometer positioning system. Experimental results proved validity of the algorithm.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2006年第2期135-138,共4页
Transactions of Beijing Institute of Technology
基金
国家自然科学基金资助项目(10402003)
北京理工大学基础研究基金资助项目(200301F10)
关键词
混沌神经网络
压电陶瓷
迟滞模型
纳米定位系统
chaotic neural network
piezoceramics
hysteresis model
nanometer positioning system
