Mcc Toolbox →

quad = mbcquadratic(train, 'Response', 'NOx', 'Predictors', 'Speed','Load','Timing'); quad = fit(quad); summary(quad); % See coefficients Use the calset object to optimize lookup tables for multiple responses (e.g., minimize BSFC, keep NOx < limit).

% 4. Optimize timing cal = calset(torque_model, 'Goal','maximize', 'Response','Torque'); cal = addconstraint(cal, 'pred(knock_model) <= 0.1'); % knock probability <10% cal = setfactorrange(cal, 'Timing', -10, 30); optimal = optimize(cal); mcc toolbox

% 1. Load data load engine_data.mat % contains Speed, Load, Timing, Torque, Knock % 2. Build torque model torque_model = mbcgp(data, 'Torque', 'Speed','Load','Timing'); torque_model = fit(torque_model); quad = mbcquadratic(train

% 3. Build knock model (binary: 0=no knock, 1=knock) knock_model = mbcgp(data, 'Knock', 'Speed','Load','Timing', 'Distribution','binomial'); knock_model = fit(knock_model); quad = fit(quad)