Artificial Immune System MATLAB code for download

Description

Artificial Immune Network ( aiNet )

% function [M,D] = ainet(Ag,Ab,Agt,n,N,gen)
% M -> memory cells matrix
% D -> distance matrix for M
% Ag -> antigens (training patterns)
% Ab -> network antibodies
% n -> no. of best-matching cells taken for each Ag (Selection)
% N -> clone number multiplier
% gen -> maximum number of generations
%
% L -> Ag and Ab length
% N1 -> no. of antibodies (constructive)
% N2 -> no. of antigens
% Nc -> no. of clones to be generated
% D -> Ag-Ab affinity vector
% Do -> D sorted in ascending order
% mi -> learning (hypermutation) rate (default: 4.0)
% qi -> percentile amount of clones to be Re-selected
% ts -> suppression threshold (default: 0.001)
% tp -> pruning threshold
% D1 -> idiotypic affinity matrix [N1,N1]
% vbD -> vector best affinity for each Ag
% nR -> no. of Ab to be re-selected

Pattern Recognition in the Immune System using a Growing SOM

% Main features: bipolar weights, Hamming Distance, Winner takes all
% PHASE I: Growing followed by Pruning
% PHASE II: Supervised Evolution
%
% function [w,win,cwin,D] = hybrid(ag,eps,comp,alfa,beta,pc,pm),
% w -> weight matrix (Ab population)
% win -> winner for each Ag (v)
% cwin -> amount of winning of each individual (tau)
% D -> hamming distance of each Ag with relation to its mapped class
% ag -> antigen population to be recognized (n2xs2)
% eps -> ball of stimulation
% comp -> comparison: 1 for comparing complementary chains
% 0 for comparing identical chains (Hamm. dist.)
% alfa -> amount of bits to be changed
% beta -> number of iterations for reducing the learning rate
%
% Auxiliar functions: COVER, UPDATE, SPLIT, PRUNE, MATCH, CADEIA, TESTGSOM