https://www.mathworks.com/help/curvefit/surface-fitting.html

There are multiple ways to fit a curve in MATLAB. You can use "fit" function or "polyfit". However, you need to specify the degree of the polynomial.

You can also try the "fitlm" function with each of its options until you achieve a minimum adjustment error.

Sir, try this package:

https://www.mathworks.com/matlabcentral/fileexchange/57329-lsclogarithmic-xi-yi-zi-x-y-z-c0-d-t-n-g-

can anyone suggest how to implement lest square for same problem in matlab, without using any direct curve fitting function?

The recursive least squares algorithm (RLS) allows for (real-time) dynamical application of least squares (LS) regression to a time series of time-stamped continuously acquired data points. For the RLS algorithm with forgetting factor (RLS-FF), acquired data is weighted according to its age, with increased weight given to the most recent data. This is often convenient for adaptive control and/or real-time optimization.

Application example for a 3D linearized correlation ― the KLa correlation, used to predict the O2 gas-liquid mass-transfer ― In the signalled work (MSc Thesis), while investigating adaptive control and energetic optimization of aerobic fermenters, I have applied the RLS-FF algorithm to estimate the parameters from that correlation. Estimates were improved by imposing sinusoidal disturbance to air flow and agitation speed (manipulated variables). The power dissipated by agitation was accessed by a torque meter (pilot plant). Simulations, carried in Excel 5.0 with Visual Basic for Applications (VBA) macros, accessed the effect of numerically generated white Gaussian noise (2-sigma truncated) and of first order delay:

Thesis Controlo do Oxigénio Dissolvido em Fermentadores para Minimi...