This study is dedicated to developing a fuzzy neural network with linguisti
c teaching signals. The proposed network, which can be applied either as a
fuzzy expert system or a fuzzy controller, is able to process and learn the
numerical information as well as the linguistic information. The network c
onsists of two parts : (1) initial weights generation and (2) error back-pr
opagation (EBP)-type learning algorithm. In the first part, a genetic algor
ithm (GA) generates the initial weights for a fuzzy neural network in order
to prevent the network getting stuck to the local minimum. The second part
employs the EBP-type learning algorithm for fine-tuning. In addition, the
unimportant weights are eliminated during the training process. The simulat
ed results do not only indicate that the proposed network can accurately le
arn the relations of fuzzy inputs and fuzzy outputs, but also show that the
initial weights from the GA can coverage better and weight elimination rea
lly can reduce the training error. Moreover, real-world problem results sho
w that the proposed network is able to learn the fuzzy IF-THEN rules captur
ed from the retailing experts regarding the promotion effect on the sales.