multidimensional curve fitting y=mx+c
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Dear all, I can understand the problem of solving the equation y=mx+c where m and c are slope and offset in a scalar value, but how to solve the equation y=mx+c where m and c are slope and offset in a vector value. In the above problem both slope and offset are a kind of function of variables(A,B,C,D,E,F,etc), so have to change the variables to find the best m,c vectors which fits the curve y. C,E are all row vectors(not allowed to change) A,B,D,F- are scalar For example, m=A.*B.*C; c=D.*E; in this above function we have to optimize the variables A,B,D in order to get the best m and c row vectors which fits the curve.
requesting solution through With and without curve fitting toolbox if possible.
Thanks in advance.
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Samuel Vergara
2017-8-24
Your problem is to find the parameters of a linear model, is just to solve y=O*X where O is a parameter matrix. Save your several training variables in columns in a Xt matrix, and solve: Y*pinv(X)=O. Check system identification in google.
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