Fuzzy Neural Network Model Using a Fuzzy Learning Vector Quantization with the Relative Distance

In this paper, we propose a fuzzy LVQ (Iearning vector quantization) which is based on the fuzzification of LVQ. The proposed fuzzy LVQ uses the different learning rate depending on whether classification is correct or not. When the classification is correct, it uses the combination of a function of the distance between the input vector and the prototypes of classes and a function of the number of iteration as the fuzzy learning rate. On the other hand, when the classification is not correct, it uses the combination of the fuzzy membership value and a function of the number of iteration as the fuzzy learning rate. The proposed FLVQ (fuzzy LVQ) is integrated into the supervised IAFC (integrated adaptive fuzzy clustering) neural network 5. We used iris data set to compare the performance of the supervised IAFC neural network 5 with those of LVQ algorithm and back propagation neural network. The supervised IAFC neural network 5 yielded fewer misclassifications than LVQ algorithm and back propagation neural network.