Team:Paris/Modeling/More FP Algo



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function optimal_parameters = find_FP(X_data, Y_data, initial_parameters) % gives the 'best parameters' involved in f4, f5, f6, f7 or f8  % with FlhDC = 0 or FliA = 0 by least-square optimisation % X_data = vector of given values of [FliA]i or [FlhDC]i (experimentally % controled) % Y_data = vector of experimentally measured values f4, f5, f6, f7 or f8 % corresponding of the X_data % initial_parameters = values of the parameters proposed by the literature %                      or simply guessed %                   = [beta, K -> (K)/(coef), n]     function output = act_pProm(parameters, X_data) for k = 1:length(X_data) output(k) = parameters(1)*hill(X_data(k), parameters(2), parameters(3)); end end options=optimset( 'LevenbergMarquardt', 'on' , 'TolX' ,1e-10, 'MaxFunEvals' ,1e10, 'TolFun' ,1e-10, 'MaxIter' ,1e4); % options for the function lsqcurvefit optimal_parameters = lsqcurvefit( @(parameters, X_data) act_pProm(parameters, X_data),...    initial_parameters, X_data, Y_data, options ); % search for the fittest parameters, between 1/10 and 10 times the initial % parameters end