NEURAL NETWORK WITH LEARNING FUNCTION BASED ON SIMULATED ANNEALING AND MONTE-CARLO METHOD

A neural network with a learning function which does not require the backward propagation of the signals for the learning, which is applicable for a case involving the feedback of the synapses or the loop formed by the synapses, and which enables the construction of a large scale neural network by using compact and inexpensive circuit elements. An evaluation value is calculated according to a difference between each output signal of the network and a corresponding teacher signal; a manner of updating the synapse weight factor of each synapse is determined according to an evaluation value change between a present value and a previous value of evaluation value on a basis of the simulated annealing; a randomly changing update control signal is generated according to a random number; and a synapse weight factor of each synapse is updated according to the generated update control signal and the determined manner of updating on a basis of the Monte-Carlo method.