摘要
针对某些对象梯度不易计算的问题,提出一种新的优化训练方法.利用水往低处流的原理对参数进行寻优,将随机初始参数根据步长生成多维空间中点的坐标形式,用实际输出和目标输出之间的差来表示以此点为球心的球的半径,将最小半径的球作为下一步寻优的中心点.此方法具有无需计算梯度,初始值随机选取,易编程,寻优快等特点.通过仿真实验,此方法成功应用在PID控制器、LQR控制器和神经网络中,获取了最优参数,具有实际操作性.
A newtraining method was proposed to solve the problem that some gradients of the objects are not easy to calculate. This method was based on the principle of gravity optimizes parameters. Random initial parameter based on step was set as coordinate form which in the midpoint of the multidimensional space. The error between the actual output and the target output was set as radius. This method had advantages which could not need to calculate the gradient and could randomly select initial value. This method was successfully used in the PID controller,LQR controller and neural network.
出处
《东北大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2015年第11期1558-1561,共4页
Journal of Northeastern University(Natural Science)
基金
辽宁省自然科学基金资助项目(20102127)
辽宁省高校创新团队支持计划项目(2009T062.LT2010058)
关键词
重力
训练
优化
梯度
神经网络
PID控制器
LQR控制器
参数
gravity
training
optimization
gradient
neural network
PID controller
LQR controller
parameter