Nonlinear iterated function system coding using neural networks
In this paper we attempt to form a neural network to code nonlinear 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. First, we start with a given iterated function system attractor, with a random set of weights of the network. Second, we compare the consequent images using this neural network with the original image. On the basis of the result of this comparison, we can update the weight functions and the code of the nonlinear iterated function system (NLIFS). A common metric or error function used to compare between the two image fractal attractors is the Hausdorff distance. The error function gives us good means to measurement the difference between the two images.
neural networks fractals nonlinear ifs