This paper presents a self-creating neural network in which a conservation
principle is incorporated with the competitive learning algorithm to harmon
ize equi-probable and equi distortion criteria [1]. Each node is associated
with a measure of vitality which is updated after each input presentation.
The total amount of vitality in the network at any time is 1, hence the na
me conservation. Competitive learning based on a vitality conservation prin
ciple is near-optimum, in the sense that problem of trapping in a local min
imum is alleviated by adding perturbations to the learning rate during node
generation processes. Combined with a procedure that redistributes the lea
rning rate variables after generation and removal of nodes, the competitive
conservation strategy provides a novel approach to the problem of harmoniz
ing equi-error and equi probable criteria. The training process is smooth a
nd incremental, it not only achieves the biologically plausible learning pr
operty, but also facilitates systematic derivations for training parameters
. Comparison studies on learning vector quantization involving stationary a
nd nonstationary, structured and nonstructured inputs demonstrate that the
proposed network outperforms other competitive networks in terms of quantiz
ation error, learning speed, and codeword search efficiency.