Properties of learning in fuzzy ART

This paper presents some important properties of the fuzzy ART neural network algorithm. The properties described in the paper are divided into a number of categories. These include template, access, and reset properties, as well as properties related to the number of list presentations needed for weight stabilization. These properties provide numerous insights as to how fuzzy ART operates. Furthermore, the effect of the fuzzy ART parameters /spl alpha/ and /spl rho/ on the functionality of the algorithm is clearly illustrated.<>