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

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). mcc toolbox

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

% 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); gp = fit(gp)