An Optimal PID Control Algorithm for Training Feedforward Neural Networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number6185668
Pages (from-to)2273-2283
Journal / PublicationIEEE Transactions on Industrial Electronics
Issue number6
Online published17 Apr 2012
Publication statusPublished - Jun 2013
Externally publishedYes


The training problem of feedforward neural networks (FNNs) is formulated into a proportional integral and derivative (PID) control problem of a linear discrete dynamic system in terms of the estimation error. The robust control approach greatly facilitates the analysis and design of robust learning algorithms for multiple-input-multiple-output (MIMO) FNNs using robust control methods. The drawbacks of some existing learning algorithms can therefore be revealed clearly, and an optimal robust PID-learning algorithm is developed. The optimal learning parameters can be found by utilizing linear matrix inequality optimization techniques. Theoretical analysis and examples including function approximation, system identification, exclusive-or (XOR) and encoder problems are provided to illustrate the results.

Research Area(s)

  • Feedforward neural networks, linear matrix inequality (LMI), proportional integral and derivative (PID) controller, robust learning, LEARNING ALGORITHM