Mcc Toolbox May 2026

(best for non-linear):

gp = mbcgp(train, 'Response', 'Torque', 'Predictors', 'Speed','Load'); gp = fit(gp); plot(gp); % Check fit : mcc toolbox

% 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); writecfile(table, 'calibration_table

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). (best for non-linear): gp = mbcgp(train

% Create a 2D lookup table lut = mbc2dlookup('filename.slx', table); writeblock(lut); Or generate C-array:

data = mbcdata.import('engine_test.csv'); % Remove outliers data = removeoutliers(data, 'Response', 'BSFC'); % Split into training/validation [train, val] = splitdata(data, 0.8); Use mbcmodels to create response surface models.

writecfile(table, 'calibration_table.c'); | Object/Function | Purpose | |----------------|---------| | xydesign | Generate DOE points | | mbcdata | Manage experimental data | | mbcgp , mbcquadratic | Build models | | calset | Multi-objective optimization | | mbc2dlookup | Export to Simulink | | crossvalidate | Validate model accuracy | 4. Practical Example: Engine Calibration Goal: Calibrate spark timing for max torque while limiting knock.