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Performance of a sensory-neural network developed for diagnosing of diseases is described. Information about patient's condition is provided by answers to the questionnaire. Questions correspond to sensors generating signals when patients acknowledge symptoms. These signals excite neurons in which characteristics of the diseases are represented by synaptic weights associated with indicators of symptoms. The disease corresponding to the most excited neuron is proposed as the result of diagnosing. Its reliability is estimated by the likelihood defined by the ratio of excitation of the most excited neuron and the complete neural network., Comment: International symposium on neural networks 2018, Minsk, Belarus, June 25-28, 2018

In the article a transition from pattern evolution equation of reaction-diffusion type to a cellular automaton (CA) is described. The applicability of CA is demonstrated by generating patterns of complex irregular structure on a hexagonal and quadratic lattice. With this aim a random initial field is transformed by a sequence of CA actions into a new pattern. On the hexagonal lattice this pattern resembles a lizard skin. The properties of CA are specified by the most simple majority rule that adapts selected cell state to the most frequent state of cells in its surrounding. The method could be of interest for manufacturing of textiles as well as for modeling of patterns on skin of various animals., Comment: 8th Conference on Information and Graphic Arts Technology, Ljubljana, Slovenia, 7-8 June 2018, Abstracts, R. URBAS, N. PU\v{S}NIK (eds.), Publ.: Uni. Ljubljana, Faculty of Nat. Sci. and Eng., Dept., pp. 95-96

Statistical modeling of physical laws connects experiments with mathematical descriptions of natural phenomena. The modeling is based on the probability density of measured variables expressed by experimental data via a kernel estimator. As an objective kernel the scattering function determined by calibration of the instrument is introduced. This function provides for a new definition of experimental information and redundancy of experimentation in terms of information entropy. The redundancy increases with the number of experiments, while the experimental information converges to a value that describes the complexity of the data. The difference between the redundancy and the experimental information is proposed as the model cost function. From its minimum, a proper number of data in the model is estimated. As an optimal, nonparametric estimator of the relation between measured variables the conditional average extracted from the kernel estimator is proposed. The modeling is demonstrated on noisy chaotic data., Comment: an article submitted to "IEEE Transactions on Information Theory"