A functional measure of rouchness
From: Roger Bagula (tftn_at_earthlink.net)
Date: 08/21/04
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Date: Sat, 21 Aug 2004 16:52:20 GMT
In thinking of a way to get a better than Lyapunov , Hausdorff or
Kolmogorov
measure of dimension , I thought of this:
F(curve)=0 if smooth and continuous
F(curve)<>0 if rough or discontinuous
The best measure of dimensional roughness (Mandelbrot's way of
expressing it) is the
Lyapunov exponent (or maybe the Hurst exponent?).
Box counting or capacity/ entropy dimension of the Kolmogorov type
is too big most of the time
while Hausdorff being very cut-off measure like
is usually too small.
The trouble with Lyapunov is that it depends on a derivative
and unless you are talking about a fractional derivative,
many fractal functions are of the Weierstrass fractal type
where the classical derivative doesn't exist.
I did some work on Bezier functions in IFS in the past
and fractional partial derivatives of an angular sort as well.
I came to realize that the three point Bezier function of an iterative
sequence in n:
Bezier[p,n]=p^2*f(n+2+2*p*(1-p)*f(n+1)+(1-p)^2*f(n)
is such that if smooth and continuous:
f(n+1)=Bezier[1/2,n]=f(n+2)/4+f(n+1)/2+f(n)/4
So that the function :
delta[n]=f(n+2)/4+f(n+1)/2+f(n)/4-f(n+1)
is a measure of the roughness.
Putting this measure in an Lyapunov average type function:
Measure[n]=Sum[Log[1+delta[i]],{i,1,n}]/n
I tried this out by comparing it to a known rough set, the primes
and it's Lyapunov integer difference average.
In this experiment the new Bezier roughness measure performs better than the
Lyapunov equivalent over the same range in detecting roughness.
Respectfully, Roger L. Bagula
tftn@earthlink.net, 11759Waterhill Road, Lakeside,Ca 92040-2905,tel: 619-5610814 :
URL : http://home.earthlink.net/~tftn
URL : http://victorian.fortunecity.com/carmelita/435/
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