Accumulative Learning using Multiple ANN for Flexible Link Control

This paper presents a scheme of multiple neural networks (MNNs) with a new strategy of combination. This combination can obtain an accumulative learning: the knowledge is increased by gradually adding more neural networks to the system. This scheme is applied to flexible link control via feedback-error-learning (FEL) strategy, here called multi-network-feedback-error-learning. Three different neural control approaches are used to control a flexible link, and it is shown that a better inverse dynamic model of the plant is obtained in this case.