genetic algorithm for curve fitting

1 次查看（过去 30 天）
Proman
Proman 2020-6-28
评论： Star Strider 2020-6-28
Hello Everyone
I want to Fit a curve (called MFit) on another curve (called M)
MFit is a function and defined by the following relation:
MFit = M0 + c0 * (h * z - log(z - z0 / z0))
and M is a 100-element vector. I want to fit MFit on M by choosing the right value of c0. z is a 100-element vector and M0,h and z0 are constants. What I have in mind is to define a target function as
Fun1 = abs(M - MFit)
so that by minimzing it, MFit will be fit. This is my proposed method:
MFit = @(c0) (M0 + c0 * (z - h * log((z + z0) / z0)));
Fun1 = @(c0) abs(M - MFit);
rng default
C0 = ga(Fun1,1);
but things go wrong when I run the code. Can anybody help me how I may solve this problem with genetic algorithm?

请先登录，再回答此问题。

采纳的回答

Star Strider
Star Strider 2020-6-28
I would do something like this (with ‘M’ and the constants already existing in your workspace):
MFit = @(c0,M0,h,z,z0) (M0 + c0 * (z - h * log((z + z0) / z0)));
Fun1 = @(c0) norm(M - MFit(c0,M0,h,z,z0));
c0_est = ga(Fun1, 1);
The fitness funciton must return a scalar value. (The ga call can be further optimised by using an optons structure.)
.
  4 个评论
显示 2更早的评论
Proman
Proman 2020-6-28
Many thanks for your productive direction Sir

请先登录，再进行评论。

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Genetic Algorithm 的更多信息

标签

产品


版本

R2019b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by