TY  - JOUR
T1  - Solving the Inverse Problem of 3D Fractals Using Neural Networks
AU - Al-Jawfi, Rashad A. 
JO  - International Journal of System Signal Control and Engineering Application
VL  - 11
IS  - 2
SP  - 30
EP  - 34
PY  - 2018
DA  - 2001/08/19
SN  - 1997-5422
DO  - ijssceapp.2018.30.34
UR  - https://makhillpublications.co/view-article.php?doi=ijssceapp.2018.30.34
KW  - finding
KW  -the inverse problem
KW  -neural networks
KW  -fractal
KW  -3D IFS
KW  -measurement
AB  - In this research, we formed a neural network to coding homogeneous iterated function system. Our
approach to this problem consists of finding an error function which will be minimized when the network coded
attractor is equal to the desired attractor. Firstly, we start with a given fractal attractor find a set of weights for
the network which will approximate the attractor. Secondly, we compare the consequent image using this neural
network with the original image with the result of this comparison we can update the weight functions and the
code of Iterated Function System (IFS). A common metric or error function used to compare between the two
image fractal attractors is the Hausdorff distance. The error function gets us good means to measurement the
difference between the two images. The distance is calculated by finding the farthest point on each set relative
to the other set and returning the maximum of these two distances.
ER  -