摘要
粗糙控制是近年来兴起的一种新的智能控制方法,作为对粗糙控制理论的探索,提出了粗糙规则逆模型的概念,并分析了粗糙规则逆模型的一致性和完备性问题,引入了基于径向基函数网络的粗糙决策规则推理方法,构造了粗糙-神经网络逆模型。对粗糙-神经网络逆系统模型的辨识以及基于粗糙-神经网络逆模型的控制理论和方法进行了分析和讨论,并通过实例仿真计算与实验分析,验证了粗糙-神经网络逆模型控制方法的可行性。
Rough control is a new intelligent control method that rose in recent years. As an exploration of rough control theory, the concept of rough rule inverse model is fast put forward. The consistency and completeness of rough rule inverse model are analyzed, and a rough decision rule reasoning method based on radial basis function （RBF） neural network is introduced. On this basis, the rough-neural inverse model is presented. And then the identification of rough-neural inverse system, the control theory and method based on rough-neural inverse model are researched in detail. The feasibility of the proposed control method is demonstrated by simulation and experiment analysis.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2009年第8期1726-1733,共8页
Chinese Journal of Scientific Instrument
基金
江苏省教育厅高校自然科学基金基础研究项目(08KJB520007)
南京邮电大学引进人才科研基金项目(NY207148)资助
关键词
粗糙集
神经网络
决策规则
逆模型
粗糙控制
rough set
neural network
decision rule
inverse model
rough control
