摘要
介绍一种基于规则的自学习神经网络控制器在采掘机器人上的应用．它根据实时执行的结果，采用多步学习－模糊监督学习方法，修正神经网络的教师信号，使控制算法简化，提高了计算的实时性。
A method of rule based self learning neural network controller applied to the robotic excavator is presented. According to the real time control result, the controller takes advantage of the method of multiple steps learning and fuzzy supervision to correct the teacher signals of neural network, therefore, the control algorithm becomes simple, the reality of calculating is improved, and the learning speed is increased. This method is tested and verified by experiments.
出处
《机器人》
EI
CSCD
北大核心
1996年第5期316-320,共5页
Robot
基金
国家教委博士点基金项目
浙江大学流体传动与控制国家重点实验室资助项目.
关键词
采掘机器人
神经网络
控制器
机器人
Robotic excavator, fuzzy control, neural network control
