Fuzzy ART and Fuzzy ARTMAP Neural Networks
22 Dec 2003
24 Dec 2003)
This package allows creation, training, and testing of ART and ARTMAP neural networks.
|ART_Update_Weights(input, weight, categoryNumber, learningRate)
function [updatedWeight, weightChange] = ART_Update_Weights(input, weight, categoryNumber, learningRate)
% ART_Update_Weights Updates the weight matrix of an ART network.
% [UPDATEDWEIGHT, WEIGHTCHANGE] = ART_Update_Weights(INPUT, WEIGHT, CATEGORYNUMBER, LEARNINGRATE)
% This function returns a new weight matrix that has "learned" the input
% in the given category, as well as a value correspoding to whether or
% not the weight matrix was changed (0 = no change; 1 = change).
% The input parameters are as follows:
% The INPUT is a vector of size NumFeatures that contains the input
% signal into the network. The WEIGHT is a matrix of size
% NumFeatures-by-NumCategories which holds the weights of the network.
% The CATEGORYNUMBER is the number of the category that codes the
% current input. The LEARNINGRATE is the rate at which the network
% should learn new inputs. The length of the INPUT vector must equal
% the number of rows in the WEIGHT matrix, the CATEGORYNUMBER must
% be in the range [1, NumCategories], and the LEARNINGRATE must be
% in the range [0, 1].
% The return parameters are as follows:
% The UPDATEDWEIGHT is a matrix of size NumFeatures-by-NumCategories
% that holds the new weights of the network after the input has been
% successfully learned.
% The WEIGHTCHANGE is a value (0 or 1) which relays whether or not
% the weight matrix was changed during this updating. Here, 0 represents
% no change and 1 represents a change.
% Get the number of features from the weight matrix.
[numFeatures, numCategories] = size(weight);
% Check the input parameters for correct ranges.
if(length(input) ~= numFeatures)
error('The length of the input and rows of the weights do not match.');
if((categoryNumber < 1) | (categoryNumber > numCategories))
error('The category number must be in the range [1, NumCategories].');
if((learningRate < 0) | (learningRate > 1))
error('The learning rate must be within the range [0, 1].');
% Modify the appropriate category of the weight matrix according to the following rule:
% FOR EACH i IN input
% IF input(i) < weight(i)(j) THEN
% weight(i)(j) = (a * input(i)) + ((1 - a) * weight(i)(j))
% weight(i)(j) does not change
% END IF
% END FOR
% where "a" is the learning rate and "j" represents the appropriate category.
weightChange = 0;
for i = 1:numFeatures
if(input(i) < weight(i, categoryNumber))
weight(i, categoryNumber) = (learningRate * input(i)) + ((1 - learningRate) * weight(i, categoryNumber));
weightChange = 1;
% Return the updated weight matrix.
updatedWeight = weight;