Learning of associative memory in form of neural network suitable for connectionist model

摘要

The learning of an associative memory suitable for the connectionist model which can deal with the patterns having the non- random frequencies of the appearances or the non-random correlations. In this invention, the learning of the associative memory in a form of a neural network, in which a plurality of nodes having activation values are connected by a plurality of links having link weight values, is achieved by entering a plurality of learning patterns sequentially, where each learning pattern has a plurality of elements in correspondence with the nodes, calculating an energy E of the entered learning pattern, determining a learning amount &dgr; for the entered learning pattern according to a difference between the calculated energy E and a predetermined reference energy level Eth, and updating the link weight values of the links according to the entered learning pattern and the determined learning amount &dgr;.