Dynamic Knowledge Increase of Associative Memory for Many to Many Based on Incidence of Patterns

Dynamic knowledge increase of associative memory is essential for practical applications of artificial neural networks. However existing discrete bipolar neural networks have no properties to achieve this aim. In this paper, we proposed an new multi module associative memory model for many to many associations based on incidence of patterns, through adding new neurons and connections to neural networks of this model, which may increase knowledge dynamically as well as not forget information stored before. The properties of dynamic knowledge increase in new associative memory are investigated in detail.