最小均方根数据拟合源码matlab程序，提供操作录像，matlab2021a版本使用

function [ YI ] = lsq_lut_piecewise( x, y, XI ) % LSQ_LUT_PIECEWISE Piecewise linear interpolation for 1-D interpolation (table lookup) % YI = lsq_lut_piecewise( x, y, XI ) obtain optimal (least-square sense) % vector to be used with linear interpolation routine. % The target is finding Y given X the minimization of function % f = |y-interp1(XI,YI,x)|^2 % % INPUT % x measured data vector % y measured data vector % XI break points of 1-D table % % OUTPUT % YI interpolation points of 1-D table % y = interp1(XI,YI,x) % if size(x,2) ~= 1 error('Vector x must have dimension n x 1.'); elseif size(y,2) ~= 1 error('Vector y must have dimension n x 1.'); elseif size(x,1) ~= size(x,1) error('Vector x and y must have dimension n x 1.'); end % matrix defined by x measurements A = sparse([]); % vector for y measurements Y = []; for j=2:length(XI) % get index of points in bin [XI(j-1) XI(j)] ix = x>=XI(j-1) & x<XI(j); % check if we have data points in bin if ~any(ix) warning(sprintf('Bin [%f %f] has no data points, check estimation. Please re-define X vector accordingly.',XI(j-1),XI(j))); end % get x and y data subset x_ = x(ix); y_ = y(ix); % create temporary matrix to be added to A tmp = [(( -x_+XI(j-1) ) / ( XI(j)-XI(j-1) ) + 1) (( x_-XI(j-1) ) / ( XI(j)-XI(j-1) ))]; % build matrix of measurement with constraints [m1,n1]=size(A); [m2,n2]=size(tmp); A = [[A zeros(m1,n2-1)];[zeros(m2,n1-1) tmp]]; % concatenate y measurements of bin Y = [Y; y_]; end % obtain least-squares Y estimation YI=A\Y;