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The word "fractal" has two related meanings. In colloquial usage, it denotes a shape that is recursively constructed or self-similar, that is, a shape that appears similar at all scales of magnification and is therefore often referred to as "infinitely complex." In Mathematics a fractal is a Geometric object that satisfies a specific technical condition, namely having a Hausdorff Dimension greater than its Topological Dimension . The term ''fractal'' was coined in 1975 by Benoît Mandelbrot , from the Latin ''fractus,'' meaning "broken" or "fractured."


EXAMPLES

, a fractal related to the Mandelbrot set]]

A relatively simple class of examples is the Cantor Set s, in which short and then shorter (open) intervals are struck out of the Unit Interval 1 , leaving a set that might (or might not) actually be self-similar under enlargement, and might (or might not) have dimension ''d'' that has 0 < ''d'' < 1. A simple recipe, such as excluding the Digit ''7'' from Decimal Representation s, is self-similar under 10-fold Enlargement , and also has dimension log 9/log 10 (this value is the same, no matter what Logarithm ic base is chosen), showing the connection of the two concepts.

Additional examples of fractals include the Lyapunov Fractal , Sierpinski Triangle and Carpet , Menger Sponge , Dragon Curve , Space-filling Curve , limit sets of Kleinian Group s, and the Koch Curve . Fractals can be Deterministic or Stochastic (i.e. non-deterministic).

Chaotic Dynamical Systems are sometimes associated with fractals. Objects in the phase space of a dynamical system can be fractals (see Attractor ). Objects in the parameter space for a family of systems may be fractal as well. An interesting example is the Mandelbrot set. This set contains whole discs, so has dimension 2 and is not fractal--but what is truly surprising is that the '' Boundary '' of the Mandelbrot set also has a Hausdorff dimension of 2. (M. Shishikura proved that in 1991.)


The fractional dimension of the boundary of the Koch snowflake

The following analysis of the Koch Snowflake suggests how self-similarity can be used to analyze fractal properties. This argument is only a sketch, but provides some of the flavor of the field.

The total length of a number, ''N'', of small steps, ''L'', is the product ''NL''. Applied to the boundary of the Koch snowflake this gives a boundless length as ''L'' approaches zero. But this distinction is not satisfactory, as different Koch snowflakes do have different sizes. A solution is to measure, not in meter, m, nor in square meter, m2, but in some other power of a meter, m''x''. Now 4''N''(''L''/3)''x'' = ''NL''''x'', because a three times shorter steplength requires four times as many steps, as is seen from the figure. Solving that equation gives ''x'' = (log 4)/(log 3) ≈ 1.26186. So the unit of measurement of the boundary of the Koch snowflake is approximately m1.26186.





Even 2000 times magnification of the Mandelbrot set uncovers fine detail resembling the full set.


Three common techniques for generating fractals are:


Fractals can also be classified according to their self-similarity. There are three types of self-similarity found in fractals:

  • Exact self-similarity — This is the strongest type of self-similarity; the fractal appears identical at different scales. Fractals defined by iterated function systems often display exact self-similarity.

  • Quasi-self-similarity — This is a loose form of self-similarity; the fractal appears approximately (but not exactly) identical at different scales. Quasi-self-similar fractals contain small copies of the entire fractal in distorted and degenerate forms. Fractals defined by Recurrence Relation s are usually quasi-self-similar but not exactly self-similar.

  • Statistical self-similarity — This is the weakest type of self-similarity; the fractal has numerical or statistical measures which are preserved across scales. Most reasonable definitions of "fractal" trivially imply some form of statistical self-similarity. (Fractal dimension itself is a numerical measure which is preserved across scales.) Random fractals are examples of fractals which are statistically self-similar, but neither exactly nor quasi-self-similar.


It should be noted that not all self-similar objects are fractals — e.g., the Real Line (a straight Euclidean line) is exactly self-similar, but since its Hausdorff dimension and topological dimension are both equal to one, it is not a fractal.


FRACTALS IN NATURE

s]]
Approximate fractals are easily found in nature. These objects display self-similar structure over an extended, but finite, scale range. Examples include Cloud s, Snow Flakes , Mountain s, River networks, and systems of Blood Vessel s.

Trees and ferns are fractal in nature and can be modeled on a computer using a Recursive Algorithm . This recursive nature is clear in these examples — a branch from a tree or a Frond from a fern is a miniature replica of the whole: not identical, but similar in nature.

The surface of a mountain can be modeled on a computer using a fractal: Start with a triangle in 3D space and connect the central points of each side by line segments, resulting in 4 triangles. The central points are then randomly moved up or down, within a defined range. The procedure is repeated, decreasing at each iteration the range by half. The recursive nature of the algorithm guarantees that the whole is statistically similar to each detail.











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