On-line Learning Algorithm Based on Signal Flow Graph Theory for PID Neural Networks

摘要：

It was difficult to design a simple and effective learning algorithm based on gradient for PID neural networks because their neurons have discontinuous transfer functions. A new on-line algorithm was proposed according to the signal flow graph (SFG) theory in this paper. All gradients could be calculated directly from the SFGs of PID neural networks by this method. Moreover, an adaptive learning rate was designed to guarantee the convergence of the algorithm by Lyapunovs stability theory. Simulation results show the algorithm is an effective on-line learning algorithm for PID neural networks in nonlinear dynamic system identification.

关键词： PID neural network Signal flow graph Learning algorithm

作者: Li Ming Yang Cheng Shu Yu Yang Cheng-wu

作者单位: College of Communication, Machinery and Civil Engineering, Southwest Forestry University, Kunming 65 College of Power Engineering, Nanjing University of Science & Technology, Nanjing 210094

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 3235-3238

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）