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 input

• Type: Journal
• Year: 2006
• Where: IJON
• Authors: Jie Ni, Qing Song
• Keywords: Control System, IJON 2006, Neural, Neural Network