A neural network processing method and apparatus interconnects a plurality of artificial neurons (11) and sets a weight between the artificial neurons according to the instruction image pattern. If the number of artificial neurons generating the maximum output value is expected to be smaller than the number of the artificial neurons generating the minimum output value, the function f, which is defined as (f(x)>-f(-x)) for a positive value x, is set for each artificial neuron. If the number of the artificial neurons generating the maximum output value is expected to be larger than the number of the artificial neurons for generating the minimum output value, another function f, which is defined as (f(x)<-f(-x)) for a positive value x, is set for each artificial neuron. Then, the external input signals are supplied to the artificial neurons according to the unknown image pattern. The artificial neurons are activated to enter the input signal as a weighted sum of the other artificial neuron's output signal and corresponding weight between artificial neurons, and to generate the output signal. Therefore, when the neural network is in a stable state, the output signals of the artificial neurons correspond to the associated image pattern as for the unknown image pattern. <IMAGE>
NEURAL NETWORK PROCESSING METHOD AND APPARATUS