Dynamic pruning algorithm for multilayer perceptron based neural control systems
Generalization ability of neural networks is very important and a rule of thumb for good generalization in neural systems is that the smallest system should be used to fit the training data. Unfortunately, it is normally difficult to determine the optimal size of networks, particularly, in the sequential training applications such as online control. In this paper, an online training algorithm with a dynamic pruning procedure is proposed for the online tuning and pruning the neural tracking control system. The conic sector theory is introduced in the design of this robust neural control system, which aims at providing guaranteed boundedness for both the inputoutput signals and the weights of the neural network. The proposed algorithm is applied to a multilayer perceptron with adjustable weights and a complete convergence proof is provided. The neural control system guarantees the closed-loop stability of the estimation, and in turn, a good tracking performance. The performance improvement of the proposed system over existing systems can be qualified in terms of better generalization ability, preventing weight shifts, fast convergence and robustness against system disturbance.
dynamic pruning multilayer perceptron neural network conic sector robust control