Robust backpropagation training algorithm for multilayered neural tracking controller
Qing Hua Song, Jizhong Xiao, Yeng Chai Soh · IEEE Transactions on Neural Networks · 1999
A robust backpropagation training algorithm with a dead zone scheme is used for the online tuning of the neuralnetwork (NN) tracking control system. This assures the convergence of the multilayered NN in the presence of disturbance. It is proved in this paper that the selection of a smaller range of the dead zone leads to a smaller estimate error of the NN, and hence a smaller tracking error of the NN tracking controller. The proposed algorithm is applied to a three-layered network with adjustable weights and a complete convergence proof is provided. The results can also be extended to the network with more hidden layers.