ASDM-a novel neural network model based on sparse distributed memory
W.K. Tsai, A.G. Parlos, Benito R Fernandez · 1990
A novel artificial neural network model based on P. Kanerva's (MIT Press, 1988) sparse distributed memory (SDM) is presented. The model possesses many major advantages over the original SDM. It is adaptive in the sense that the memory cells are called into service or released from service by a global mechanism. Since memory cells are utilized depending on the load on the memory, the actual number of memory cells needed to implement the ASDN (adaptive SDM) is significantly smaller than what is required for the original SDM. The storing and retrieval procedures are much simpler, and analysis of the best match problem can be carried out in a deterministic setting. All the advantages of the original SDM are retained while the main drawback of the original SDM, namely, the huge number of physical memory locations, is removed. The concept of time-varying intensity of memory is introduced, and customized metrics for determining distance between two data objects are allowed