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An Optimal PID Control Algorithm for Training Feedforward Neural Networks

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2 Author(s)
Xingjian Jing ; Dept. of Mech. Eng., Hong Kong Polytech. Univ., Kowloon, China ; Li Cheng

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.

Published in:

Industrial Electronics, IEEE Transactions on  (Volume:60 ,  Issue: 6 )