Solving Coupled Differential Equations (in Matlab)

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SUMMARY

The discussion focuses on solving a set of coupled linear differential equations using MATLAB. The equations are defined as dy/dt = -a*y(t) + b*x(t) + (m-y(t))*r and dx/dt = a*y(t) - b*x(t) + (n-x(t))*r, where m, n, and r are constants. The objective is to determine the coefficients 'a' and 'b' that fit the system based on given values of y(t) and x(t) over time. The solution involves minimizing the square error between the theoretical and observed values of (x,y)(t).

PREREQUISITES
  • Understanding of coupled differential equations
  • Familiarity with MATLAB for numerical analysis
  • Knowledge of optimization techniques, specifically least squares minimization
  • Basic concepts of linear algebra related to systems of equations
NEXT STEPS
  • Learn MATLAB's ODE solvers, such as ode45, for solving differential equations
  • Research least squares fitting techniques in MATLAB for parameter estimation
  • Explore MATLAB's optimization toolbox for advanced fitting methods
  • Study linear algebra concepts relevant to solving systems of equations
USEFUL FOR

This discussion is beneficial for mathematicians, engineers, and researchers working with differential equations, particularly those using MATLAB for modeling and simulation. It is also useful for anyone involved in parameter estimation and optimization in mathematical models.

ksmanis
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Hi,

I have the following problem to solve. My system can be defined as a set of coupled differential equations as described below:

dy/dt = -a*y(t) + b*x(t) + (m-y(t))*r;
dx/dt = a*y(t) - b*x(t) + (n-x(t))*r;

where m,n and r are constants. I have the values of y(t) and x(t) for different values of t. The above dy/dt and dx/dt are based on theoretical interpretation (and I do not have their values). For the given set of x(t), y(t), I would like to find the coefficients 'a' and 'b' fitting the above system.

Appreciate your help in solving this problem in any package (preferably matlab)

Kind Regards
Subbu
 
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The ODE is linear with constant coefficients, so can be solved analytically to obtain [itex](x,y)(t)[/itex] expressly. One could then attempt to minimize the square error [itex]\sum_i \|(x,y)(t_i) - (x_i,y_i)\|^2[/itex] in the usual way.
 

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