Since typical Hamming networks has not solved the problem of pattern overlap and convergence

and not made full use of the near degree information of the input pattern with other neurons in the network

a fuzzy Hamming neural networks (FHNN) is proposed in this paper.FHNN replaces matching subnet with a fuzzy class membership subnet to solve problems of pattern overlap.It is also a three-layer feed-forward network.The

n

elements of the input pattern are presented in parallel to the n nodes of the first layer of the subnet.The number of the nodes in the hidden layer is equal to the amount of the exemplar patterns

and the weights of hidden neurons are the components of exemplar.And the number of the nodes in the output layer is equal to that of the classes to classify.The weights are the fuzzy class membership function of exemplar pattern to the classes.Only the training of the threshold T of the hidden layer and the fuzzy class membership weigh

ts of the output layer are needed.FHNN replaces competitive subnet with a comparing subnet to solve the problem of not converging and having too many interconnections.The fuzzy membership degrees of an unknown pattern to the classes are compared in parallel with a gradually decreasing reference voltage as a dynamic threshold in the comparing subnet.When the reference voltage decreases to the level of certain fuzzy class membership degree

the corresponding binary outputs skips to 1.Using modular circuit design

this network is easily extended and implemented in VLSI technology.FHNN is composed of three separate chips.The first matching chip gets the matching scores; the second calculates the fuzzy class membership degree.These two chips construct the fuzzy class membership subnet.And the third chip is a comparing subnet.As for the implementation of input mapping subnet

conventional feed-forward network circuits can be used.