Can Quotient Spaces Show Chaotic Behavior?

In summary, the logistic equation x(n+1) = rx(n)(1-x(n)) shows chaotic behavior under some values of r and is a discrete chaotic system. However, it is not possible to translate it to a continuous chaotic system as its continuous counterpart is not chaotic. To find chaos in continuous systems, we need to consider at least a three-dimensional system like the Rossler System or the Lorenz system. These systems can be studied using a Feigenbaum plot from the book "Chaos and Fractals" by Peitgen. The solution of the differential equations dx/dt = 0 and dy/dt = 1, while a line in two-dimensional space, is thought to have chaotic behavior in a quotient
  • #1
Tom Piper
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  • #2
The corresponding continuous system would be.
[tex]\frac{dx}{dt}= rx(1- x)[/tex]
where x is a continuous function of t.
 
  • #3
I wish to clarify something: The continuous counterpart of the logistic equation is not chaotic. It turns out that in order to find chaos in continuous systems, we need to consider at least a three-dimensional system. Such as the Rossler System:

[tex]x^{'}=-(y+z)[/tex]

[tex]y^{'}=x+ay[/tex]

[tex]z^{'}=b+xz-cz[/tex]

or the Lorenz system.

Hey Tom, have you ever studied these two systems? Have you drawn a Feigenbaum plot for either? I hope you have Peitgen's book, "Chaos and Fractals". That's a good reference.
 
  • #4
The "translation" I got is;
dx/dt = 0
dy/dt = 1
(Do you think they are too simple?) Although the solution of above differential equations is a line in two dimensional Euclidean space, the solution in the following quotient space;
http://geocities.com/tontokohirorin/mathematics/moduloid/fig12.jpg
is thought to have chaotic behaviour.
 
  • #5
Tom Piper said:
The "translation" I got is;
dx/dt = 0
dy/dt = 1
(Do you think they are too simple?) Although the solution of above differential equations is a line in two dimensional Euclidean space, the solution in the following quotient space;
http://geocities.com/tontokohirorin/mathematics/moduloid/fig12.jpg
is thought to have chaotic behaviour.

Yep, yep, that's not happening for me. When in doubt . . . Mathworld . . . Quotient Space . . . still didn't happen for me. You got me Tom. And that diagram your cited, what is that? Anyway, I'm not clear at all what a quotient space is and how it can model chaotic behavior. Might you provide a concrete example with pictures. Yea, pictures . . . that would be nice.:rolleyes:
 

1. What is a Continuous Logistic Map?

A Continuous Logistic Map is a mathematical function that is used to model the population growth of a species. It is based on the logistic equation, which takes into account limiting factors such as resources and competition.

2. How is a Continuous Logistic Map different from a Discrete Logistic Map?

A Continuous Logistic Map is a mathematical model that is used to describe the continuous growth of a population, while a Discrete Logistic Map is used to describe the discrete growth of a population. This means that the Continuous Logistic Map takes into account changes that occur over a continuous time period, while the Discrete Logistic Map only considers changes at specific time intervals.

3. What are the applications of Continuous Logistic Maps?

Continuous Logistic Maps have a wide range of applications in fields such as ecology, biology, economics, and finance. They are commonly used to model population growth, stock market fluctuations, and the spread of diseases.

4. How is the carrying capacity of a population determined in a Continuous Logistic Map?

The carrying capacity, or the maximum sustainable population size, is determined by the value of the parameter "K" in the logistic equation. This parameter represents the maximum number of individuals that can be supported by the available resources and environmental conditions.

5. What are the limitations of Continuous Logistic Maps?

One limitation of Continuous Logistic Maps is that they assume a constant environment, which is not always the case in real-world situations. They also rely on certain assumptions, such as the population growth rate being proportional to the population size, which may not always hold true. Additionally, Continuous Logistic Maps can become unstable and produce unrealistic results if the parameters are not chosen carefully.

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