I think there some thing wrong in Fuzzy ART code. Because, When I trained with first data, its categorize to n1, and the seconde data is categorize to n2, but when I combine to data to network, it carry out N group very large compaire to n1+n2.
I think there are some drawbacks. first, updating weights is not correct. second, when an input does not belong to any of the categories, the program return the -1 value. third, ART is on-line neural network that can be trained by off-line method. when we use categorization function instead of learn function, just one category is determined by the program and it is not correct. if someone can explain these drawbacks, i'll be glad.
Excellent work but I have a question. It seams that in the ART algorithm (not ARTMAP), the analog input vector requires some short of normalization. It does not function properly if the input values exceed a certain number (around 1.5). If there are input components above that value, the output of the algorithm is -1 meaning that this input does not belong to any of the available categories. Please advise of what is going on.