Standard Neural Network Model for Robust Stabilization of Recurrent Neural Networks

摘要：

The paper applies Lyapunov stability theory and SProcedure technique to investigate the robust stabilization problem of standard neural network model(SNNM). State-feedback controllers are designed to guarantee the global asymptotical stability of SNNM with norm-bounded uncertainties. The control law presented are formulated as linear matrix inequalities to be easily solved. Most of the existing recurrent neural networks can be transformed into SNNMs to be synthesized in a unified way.An example shows the effectiveness of this method.

关键词： standard neural network model recurrent neural networks robust stabilization Lyapunov stability linear matriz inequalities

作者: Shouguang Wang Liangxu Zhao Jianhai Zhang

作者单位: College of Information & Electronic engineer Zhejiang Gongshang University Hangzhou,China,310018 College of Computer Science Hangzhou Dianzi Universtiy Hangzhou,China,31OO18

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 3042-3045

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）