This study of two new total hand simulating methods for knits uses fuzzy th
eory and neural networks. One method, a neural network system trained with
a back-propagation algorithm, performs functional mapping between mechanica
l properties and the resulting total hand values of the fuzzy predicting me
thod. The second method, a fuzzy-neural network system, uses the fuzzy memb
ership function, weighted factor vector, and error back-propagation algorit
hm. The principal mechanical properties of stretchiness, bulkiness, flexibi
lity, distortion, weight, and surface roughness of the knitted fabrics are
correlated with experimentally determined Kawabata total hand values and fu
zzy transformed overall hand values. Fuzzy and neural networks agree better
with the subjective test results than the KES-FB system. The mechanical pr
operties are fuzzified by fuzzy membership functions, then trained to predi
ct the total hand value of outerwear knitted fabrics. In each case, the pre
diction error is less than the standard deviation of experimentation, and t
he optimum structure is investigated. These two systems, which use the Pasc
al programming language, produce objective ratings of outerwear knit fabric
s.