摘要
本文采用神经网络的方法,将专家在平衡—模拟倒摆小车时记录下来的数据经处理后用监督式学习的方法训练一前置式神经网络。训练后的神经网络派生出了一组专家尚未意识到或者表达不出来的规则。本文将该规则构造的专家系统控制器与使用ID3算法推导出的规则构造的专家系统控制器进行了比较。实验结果表明神经网络算法学习出来的规则较ID3算法推导出的规则更为有效且更有应用价值。
In this paper, we present a method of training a feedforward neural network using supervised learning scheme to balance an inverted pendulum and cart system. The data used to train the neural network was obtained from a human expert doing the same task. The trained neural network uncovers a set of rules which could be very difficult to derive from the human expert. Comparison was made between the neural-network learned rule and a decision tree rule deducted by Quilan's ID3 induction algorithm using the same set of data. Experiment results showed that the neural network learned rule is more robust. At the same time, we find that the neural network learned rule can be modified to do a similar and more important task-the attitude control of a rocket.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
1998年第4期365-369,共5页
Pattern Recognition and Artificial Intelligence
关键词
专家系统
神经网络
机器学习
火箭
姿态控制
Intelligent Control, Expert System, Neural Network, Machine Learning
