机构地区: 湖南大学电气与信息工程学院电气工程系
出 处: 《控制理论与应用》 1998年第5期701-707,共7页
摘 要: 本文提出一类基于高斯基神经网络的自学习控制器,该控制器由两个GPFN网络组成,一个完成PID学习控制,另一个完成未知被控对象模型的建模.为加快网络的学习过程,文中提出了递归最小二乘法(RLS)用于神经网络的学习,并分析研究了自学习控制系统的收敛性和稳定性.仿真和实验结果表明,这类智能控制是成功的. This paper presents a new self-learning controller based on Gaussian potential function neural networks. The controller consists of a control network and model network. In order to speed up learning rate, a general least squares approach for neural network learning is proposed. Convergence and stability of this self-learning system is proved. Simulation results have shown that the new controller can be successfully applied in nonliner control systems.
领 域: [自动化与计算机技术] [自动化与计算机技术]