Harmonic neural networks for on-line learning vector quantisation

Abstract

A self-creating harmonic neural network (HNN) trained with a competitive algorithm effective for on-line learning vector quantisation is presented. It is shown that by employing dual resource counters to record the activity of each node during the training process, the equi-error and equi-probable criteria can be harmonised. Training in HNNs is smooth and incremental, and it not only achieves the biologically plausible on-line learning property, but it can also avoid the stability-plasticity dilemma, the dead-node problem, and the deficiency of the local minimum. Characterising HNNs reveals the great controllability of HNNs in favouring one criterion over the other, when faced with a must-choose situation between equi-error and equi-probable. Comparison studies on teaming vector quantisation involving stationary and non-stationary, structured and non-structured inputs demonstrate that the HNN outperforms other competitive networks in terms of quantisation error, learning speed acid codeword search efficiency.