The Akaike Information Criterion (AIC) value can be calculated using the log-likelihood function and the number of parameters in the model. Here is an example-
% assume that we have a vector of observed data 'y', and a vector of predicted data 'y_pred'
% calculate the log-likelihood function for the model
n = length(y);
sigma2 = var(y-y_pred);
logLikelihood = -0.5*n*log(2*pi) - 0.5*n*log(sigma2) - (1/(2*sigma2))*sum((y-y_pred).^2);
% calculate the number of parameters in the model
numParams = ; % insert the number of parameters in your model here
% calculate the AIC value
AIC = -2*logLikelihood + 2*numParams;
