How to solve unknown constant in an equation?

HI, im new to matlab and was wondering how would you input a command to help me solve an unknown constant in an equation.

I have a bunch of x,y points which i graphed and it gave me an exponential graph shape. The equation would be y=ae^bx. how would you type an command to help solve for a, and b. any help would be great. thank you!

Answer 1

[Walter Roberson]

You would use the Curvefittting Toolbox for that. If you have that, try invoking

cftool

It is also possible to do a fit without that.

y = a * exp(b*x) implies log(y) = log(a) + b*x which is a linear fit that can be done with polyfit.

[ian chiam]

Thank you walter!

[Image Analyst]

Doing polyfit() minimizes the residuals (difference between actuals and fitted values) of the transformed equation. I'm not sure you'd get the same a and b that you'd get if you minimized the residuals on the original equation, like the a and b you'd get from using fitnlm directly as in my Answer. I doubt it. What do you think? Of course if you don't have the stats toolbox, transforming and using polyfit should give a respectable/close model. But I think, theoretically fitnlm() would be better, right?

Answer 2

[Image Analyst]

If you have the Statistics and Machine Vision Toolbox, you can use fitnlm().

% Uses fitnlm() to fit a non-linear model (an exponential growth curve) through noisy data.
% Requires the Statistics and Machine Learning Toolbox, which is where fitnlm() is contained.
% Initialization steps.
clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clear;  % Erase all existing variables. Or clearvars if you want.
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 20;

% Create the X coordinates from 0 to 20 every 0.5 units.
X = 0 : 0.5 : 20;

% Define function that the X values obey.
a = 2 % Arbitrary sample values I picked.
b = 0.4
Y = a * exp(b * X); % Get a vector.  No noise in this Y yet.

% Add noise to Y.
Y = Y + 250 * randn(1, length(Y));
% Now we have noisy training data that we can send to fitnlm().
% Plot the noisy initial data.
plot(X, Y, 'b*', 'LineWidth', 2, 'MarkerSize', 15);
grid on;

% Convert X and Y into a table, which is the form fitnlm() likes the input data to be in.
tbl = table(X', Y');
% Define the model as Y = a + exp(-b*x)
% Note how this "x" of modelfun is related to big X and big Y.
% x((:, 1) is actually X and x(:, 2) is actually Y - the first and second columns of the table.
modelfun = @(b,x) b(1) * exp(b(2)*x(:, 1));  
beta0 = [2, .4]; % Guess values to start with.  Just make your best guess - doesn't have to be exact.
% Now the next line is where the actual model computation is done.
mdl = fitnlm(tbl, modelfun, beta0);
% Now the model creation is done and the coefficients have been determined.
% YAY!!!!

% Extract the coefficient values from the the model object.
% The actual coefficients are in the "Estimate" column of the "Coefficients" table that's part of the mode.
coefficients = mdl.Coefficients{:, 'Estimate'}
% Create smoothed/regressed data using the model:
yFitted = coefficients(1) * exp(coefficients(2)*X);
% Now we're done and we can plot the smooth model as a red line going through the noisy blue markers.
hold on;
plot(X, yFitted, 'r-', 'LineWidth', 2);
grid on;
title('Exponential Regression with fitnlm()', 'FontSize', fontSize);
xlabel('X', 'FontSize', fontSize);
ylabel('Y', 'FontSize', fontSize);
legendHandle = legend('Noisy Y', 'Fitted Y', 'Location', 'north');
legendHandle.FontSize = 25;

% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0 0 1 1]);
% Get rid of tool bar and pulldown menus that are along top of figure.
% set(gcf, 'Toolbar', 'none', 'Menu', 'none');
% Give a name to the title bar.
set(gcf, 'Name', 'Demo by ImageAnalyst', 'NumberTitle', 'Off')