In this paper we discuss our approach to based on holographic techniques implementation of neuro-fuzzy predictor for processes, described by Fractal Brownian Motion (FBM) model. We use the model of the predictor as a Riemann - Stieltjes integral over the observed traffic of specific weight function. We discuss two-layered bi-directional optical neural network to find our solution. To find the weight function we use non-linearity in the correlation layer of the neural network. In our experiments we used air-photograph of forest as this kind of images demonstrates self-similarity property and can be described by the FBM model. As a first step we used approximate solution for the weight function, achieved by using binary filtering function in the correlation layer. We demonstrate experimental results and discuss directions of our future investigations.
We consider algebraic foundations of geometrical optics approximation. The consideration is aimed at optical implementation of computational intelligence models. Theory of triangular norms and measure means are used to formulate the description. The process of negative photo-registration is considered as the implementation of the negation, which generates the algebra. Three approximations of negative recording media transmittance are considered: linear, involutive, and non-involutive one. Optically realizable orders and relations of fuzzy numbers, fuzzy sets and images are considered.