摘要
从实时系统的需求出发,研究利用遗传算法实现实时模糊控制的新技术,包括规则的形成、前提参数的训练,通过对比不同的训练参数规模,讨论了训练参数规模的大小与模型“范化”之间的关系。 经检验,用遗传算法生成的模糊系统模型输入变量少、规则数目少,并且误差小于多数传统算法生成的模型,整个模型的形成过程时间空间复杂度小、易于控制,且不受经验知识的约束,适用于实时系统。
This article proposed a new technique using Genetic Algorithms to realize Real-time fuzzy control, discussed how to form the rules, to train the parameters, the relation between the training scale and the generalization of the model comparing results of different training scales. The model has less input parameters, less rules, and smaller errors than most of other models. It has advantages in both time and space complexity. It is easy to control, and has no restriction of experience knowledge. This method is applicable to Real-time system applications.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2002年第3期266-269,共4页
Journal of University of Electronic Science and Technology of China
关键词
遗传算法
模糊控制
参数训练
模型范化
建模方法
genetic algorithms
fuzzy control
parameter training
generalization ability
