% This example illustrates the computation of approximation error % bounds (function "min_max") and the use of this information for % introducing a new uncertain parameter modelling the approximation. % (See also ex_4_5.m). % Let us consider an academic example in which simplifications % can easily be guessed. Let lfrs d1 d2 d3 d4 M0 = (3*d1^5+.0001*d1*d2*d3*d4)*(1-.0001*d4^4+d2^2*d3^2); M1 = (3*d1^5)*(1+d2^2*d3^2); % M1 is considered as an approximation of M0. For computing the % approximation error, we consider the difference DeltM = M1 - M0; DeltM = minlfr(DeltM); % For computing the error bounds: [min_val,max_val,min_int,max_int] = min_max(DeltM); % The results are min_val = -9.22e-04 and max_val =8.54e-04, which means % that the approximation error varies in an interval included between these % two values. % It remains to build M3, and approximation of M0 better than M1. For % that, we replace the neglected part of M1 by an uncertainty bounded % by min_val and max_val lfrs err [min_val] [max_val] M3 = M1 + err; size(M3)