An Optimal PID Control Algorithm for Training Feedforward Neural Networks
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Article number | 6185668 |
Pages (from-to) | 2273-2283 |
Journal / Publication | IEEE Transactions on Industrial Electronics |
Volume | 60 |
Issue number | 6 |
Online published | 17 Apr 2012 |
Publication status | Published - Jun 2013 |
Externally published | Yes |
Link(s)
Abstract
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
Citation Format(s)
An Optimal PID Control Algorithm for Training Feedforward Neural Networks. / Jing, Xingjian; Cheng, Li.
In: IEEE Transactions on Industrial Electronics, Vol. 60, No. 6, 6185668, 06.2013, p. 2273-2283.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review