Team:Paris/Modeling/More f3 Algo



pre {font-size: 1.2em} span.keyword {color: #0000FF} span.comment {color: #228B22} span.string {color: #A020F0} span.untermstring {color: #B20000} span.syscmd {color: #B28C00} }

find_&#131;3 ( FliA )
function optimal_parameters = find_f3_FliA(X_data, Y_data, initial_parameters) % gives the 'best parameters' involved in f3 with OmpR = 0 by least-square optimisation % -> USE IT AFTER find_f3_OmpR % X_data = vector of given values of ( [FliA]i ) (experimentally % controled) % Y_data = vector of experimentally measured values f3 corresponding of % the X_data % initial_parameters = values of the parameters proposed by the literature %                      or simply guessed %                   = [beta22, K6 -> (K6)/(coefOmp), n6] global beta17; % parameter GIVEN BY find_f3_OmpR function output = act_pFlhDC(parameters, X_data) for k = 1:length(X_data) output(k) = beta17*(1 - hill( X_data(k), parameters(2), parameters(3))) ... + 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_pFlhDC(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

find_&#131;3 ( OmpR* )
function optimal_parameters = find_f3_OmpR(X_data, Y_data, initial_parameters) % gives the 'best parameters' involved in f3 with FliA = 0 by least-square optimisation % -> USE IT BEFORE find_f3_FliA % X_data = vector of given values of ( [OmpR]i ) (experimentally % controled) % Y_data = vector of experimentally measured values f3 corresponding of % the X_data % initial_parameters = values of the parameters proposed by the literature %                      or simply guessed %                   = [beta17, K15 -> (K15)/(coefOmp), n15] function output = act_pFlhDC(parameters, X_data) for k = 1:length(X_data) output(k) =(1 - hill( X_data(k), parameters(2), parameters(3) )) * parameters(1); 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_pFlhDC(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