How to Linearize a Function without Using Logarithms

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To linearize the function y = α*x*e^(β*x) without using logarithms, one approach is to utilize the Maclaurin series expansion for e^(β*x), discarding higher-order terms. This leads to a first-degree polynomial approximation, which simplifies the function for small values of x. Alternatively, taking the natural logarithm results in ln(y) = ln(α) + ln(x) + β*x, which can be rearranged to form a linear model. This model allows for the extraction of coefficients through linear least squares fitting, despite concerns about the dependence on multiple variables. Ultimately, the discussion highlights methods to achieve linearization effectively.
mrwest09
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Homework Statement


Linearize the following model:
y=\alpha*x*e^{\beta*x}

Homework Equations


The only relevant equations I can think of are the laws of natural logarithms.

The Attempt at a Solution


I have tried to taking the ln of both sides however that leaves me with an equation that has two term with x in it.

ln(y)=ln(a)+ln(x)+Bx

I'm sure there has to be a simple solution but I can't visualize anything without running into the same problems.
 
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mrwest09 said:

Homework Statement


Linearize the following model:
y=\alpha*x*e^{\beta*x}


Homework Equations


The only relevant equations I can think of are the laws of natural logarithms.


The Attempt at a Solution


I have tried to taking the ln of both sides however that leaves me with an equation that has two term with x in it.

ln(y)=ln(a)+ln(x)+Bx

I'm sure there has to be a simple solution but I can't visualize anything without running into the same problems.
It's possible that you're supposed to do this using a Maclaurin series representation for your function, and discard the x2 and higher degree terms.

The Maclaurin series for e\betax is
e^{\beta x} = 1 + \frac{\beta x}{1!} + \frac{(\beta x)^2}{2!} + ... + \frac{(\beta x)^n}{n!} + ...

Multiply the terms in this series by \alphax and then discard all terms in x2 or higher.

EDIT:
On second thought, there's a simpler formula that is related to the above.

If x is "close to" 0, then f(x) \approx f(0) + x*f'(0). This gives you a first degree polynomial approximation to your function.
 
Last edited:
mrwest09 said:

Homework Statement


Linearize the following model:
y=\alpha*x*e^{\beta*x}


Homework Equations


The only relevant equations I can think of are the laws of natural logarithms.

Correct:

<br /> \ln{y} = \ln{\alpha} + \ln{x} + \beta \, x<br />

So, \ln{y} - ln{x} is a linear model relative to the function x and you can use linear least squares fit to extract the value of the coefficients \ln{\alpha} (the intercept) and \beta (the slope).
 
Okay that makes some sense but if you were to fit that into your standard y=mx+b format for linear lines wouldn't your 'y' value depend on two variables? In this case wouldn't it not be linear?
This is how I am picturing the final equation:

<br /> <br /> \ln{y} - \ln{x} = \beta \, x + \ln{\alpha}<br /> <br />

y = m x + b
 
Last edited:
Yes. In:

<br /> \tilde{y} = m \tilde{x} + b<br />

we need to calculate:

<br /> \tilde{y} = \ln{y} - \ln{x}<br />

<br /> \tilde{x} = x<br />

and then:

<br /> m = \beta, \; b = \ln{\alpha}<br />
 
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