PURPOSE: To provide a method for improving the precision of an analog neuro network by which the sum of products between input vectors and stored weight patterns is calculated. CONSTITUTION: First of all, this method consists of the training of the network through the programming of a synapse in the prescribed weight pattern. The training can be performed by using any type standard learning algorithm but if possible, a back propagation learning algorithm is used. Next, the network is printed at high temperature and the weight pattern programmed in the first training is changed. This change is caysed by electric charge redistribution generated in each synapse of the network. After printing, the network is trained again and the change caused by the electric charge redistribution is compensated. By continuously repeating the printing and retraining steps, the accuracy of the neuro network can be improved to the desired level.
METHOD FOR INCREASING OF ACCURACY OF ANALOG NEURONETWORK, ETC.