摘要
本文通过更深刻和贴切地模拟专家整定控制参数的启发式过程,开发了一种高级的专家式控制参数实时自学习算法。首先解决了如何从序贯采集进来的被各种噪声和扰动污染了的控制偏差数据中识别出瞬态过程的模式、抽取其特征的问题,然后解决了如何将反映了基本反馈控制系统各种特性的瞬态过程特征映射为能进一步改善其性能的控制参数的问题。
By means of more detailed and faithful simulation of the heuristic process where the expert assign the regulator gains, an advanced real-time expert-like self-learning algorithm has been developed. The first problem solved is how to recognize the pattern of a transient process and extract its features from sequentially sampled regulation error data corrupted by various noises and disturbances. The second is how to map those features which reflect various properties of the essential feedback regulation system onto the regulator gains for further improvement of its performances.
出处
《自动化学报》
EI
CSCD
北大核心
1992年第1期1-8,共8页
Acta Automatica Sinica
关键词
算法
模式识别
自学习
专家系统
Real-time pattern recognition
real-time production systems
frame representation
regulator self-settling
repetitive learning.
