【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.更多还原

【Abstract】 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 mi-(nima) 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.更多还原