摘要
本文将专家在平衡—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，并将该规则构造的专家系统控制器与使用Ｑｕｉｎｌａｎ的ＩＤ３算法推导出的规则构造的专家系统控制器进行比较。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。本文成功地将该规则应用于火箭的姿态控制，一类似倒摆小车的问题。
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 neuralnetwork 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
《国防科技大学学报》
EI
CAS
CSCD
1998年第3期35-39,共5页
Journal of National University of Defense Technology
关键词
专家系统
神经网络
火箭
姿态控制
倒摆小车
intelligent control, expert system, neural network, machine learning
