This paper presents the development of fuzzy algorithms for learning v
ector quantization (FALVQ). These algorithms are derived by minimizing
the weighted sum of the squared Euclidean distances between an input
vector, which represents a feature vector, and the weight vectors of a
competitive learning vector quantization (LVQ) network, which represe
nt the prototypes. This formulation Leads to competitive algorithms, w
hich allow each input vector to attract all prototypes. The strength o
f attraction between each input and the prototypes is determined by a
set of membership functions, which can be selected on the basis of spe
cific criteria, A gradient-descent-based learning rule is derived for
a general class of admissible membership functions which satisfy certa
in properties. The FALVQ 1, FALVQ 2, and FALVQ 3 families of algorithm
s are developed by selecting admissible membership functions with diff
erent properties, The proposed algorithms are tested and evaluated usi
ng the IRIS data set, The efficiency of the proposed algorithms is als
o illustrated by their use in codebook design required for image compr
ession based on vector quantization.