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where yj is the antibody, zp is the antigen training pattern, and pm is the mutation rate The mutation rate is inverse proportional to the calculated a nity, fa (zp , yj ) The higher the calculated a nity, the less yj is mutated Based on the a nity (dissimilarity) of the mutated clones with the antigen pattern zp , nh % of the highest a nity mutated clones are selected as the memory set, M An antibody in M is removed if the a nity between the antibody and zp is higher than the threshold, amax The Euclidean distance metric is also used to determine the a nity between antibodies in the network A smaller Euclidean distance implies a higher degree of similarity between the two antibodies The similarity between antibodies is measured against a suppression threshold, s , resulting in elimination of the antibodies if the suppression threshold is not exceeded This results in network pruning and elimination of selfrecognizing antibodies The a nity (similarity) among antibodies in M is measured against s for removal from M M is then concatenated with the set of antibodies B The a nity among antibodies in B is measured against s for removal from B A percentage, nl %, of antibodies with the lowest a nity to zp in B is replaced with randomly generated antibodies The stopping condition of the while-loop can be one of the following [184]: 1 Setting an iteration counter: A counter can be set to determine the maximum number of iterations 2 Setting the maximum size of the network: The while-loop can be stopped when the size of the network reaches a maximum 3 Testing for convergence: The algorithm terminates when the average error between the training patterns and the antibodies in M rises after a number of consecutive iterations The formed weighted-edge graph maps clusters in the data set to network clusters The aiNet model uses the minimal spanning tree of the formed weighted-edge graph or hierarchical agglomerative clustering to determine the structure of the network clusters in the graph Some of the drawbacks of the aiNet model is the large number of parameters that need to be speci ed and that the cost of computation increases as the number of variables of a training pattern increases The minimum spanning tree will also have di culty. ssrs pdf 417 SSRS PDF417 2D Barcode Generator - IDAutomation
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