![]() Which in this case produces the same result: p_fit_h_nlinfit =ġ2. Please note that for the default case of function h you can also use nlinfit like so: p_fit_h_nlinfit = nlinfit(x, y, q, p0) ![]() The optimization for you error-function e seems to fail. ![]() H = sum((q(p, x) - y).^2) %// default minimizaton function G = sum(sqrt(abs(q(p, x) - y))) %// better take square roots of absolute values Nur Adila Faruk Senan Department of Mechanical Engineering University of California at Berkeley A brief introduction to using ode45 in MATLAB MATLAB’s standard solver for ordinary di erential. %// create the desired error-functions for minimizationĮ = sum((y.^2 - q(p, x)).^2) %// minimization functionį = sum(abs(y - q(p, x))) %// better sum over absolute values %// create model function q with parameters p(1) = k and p(2) = n Also i added the function h, which minimizes the sum of squared errors, which is the default minimization function %// Data Here comes some code, which techically does, what you want. your minimization "function" g can produce imaginary numbers, as q-y can get negative -> absolute values.Different kind of polynomial equations example is given below. ![]() The Polynomial equations don’t contain a negative power of its variables. your minimization "function" f makes no sense, as positive and negative errors can cancel each other out -> absolute values A polynomial equation/function can be quadratic, linear, quartic, cubic, and so on.your "functions" are not functions, but vectors.
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