# Fundamentals of Mathematical Modeling of Cognitive Digital Automata

V.V. Kozhevnikov

Technological Research Institute of Ulyanovsk State University, 1/4 Universitetskaya
Naberezhnaya, Ulyanovsk, 432017, Russia

Received: 31 May 2019; accepted in revised form: 18 June 2019

Abstract
An approach to the mathematical modeling of cognitive digital automata (CDA) is proposed. This approach represents a further development of the theory of digital automata and is based on the methods of mathematical modeling of conventional digital automata. At the same time, the task of formalizing the concept of cognitivity of the mathematical model of CDA comes to the fore. Cognitivity of the mathematical model, respectively, is determined by the possibility of training and generating solutions that are not provided for in the process of learning. A specific feature of the mathematical model of CDA consists in the fact that the description of the neural network (NN) structure serves as the structure diagram of digital automata, and the logical function “NOT-AND-OR” is used as the model of the neuron. In the case of the formation of feedbacks from the output to the inputs of the neurons, the model of the neuron is a binary trigger with a logical function “NOT-AND-OR” at the input. The mathematical apparatus of Petri nets (PNs) is proposed as a tool for constructing the mathematical model of CDA: marked graphs, inhibitory PNs and PNs with programmable logic. The mathematical model is constructed on the basis of the representation of CDA in the form of the state equation of PNs from the class of Murata equations (matrix equations) or a system of linear algebraic equations.
The task of formalizing the concept of cognitivity is solved as a result of the synthesis of the logic of the initial CDA structure diagram or the formation of the formula of CDA. At the same time, the possibility of forming the formula of CDA depends on the critical mass of training sets and training algorithms. Hence, the task of generating the minimum training sets for a given function or experimentally determined function of CDA bears particular importance. Prediction or generation of solutions, in its turn, is performed on the basis of the mathematical model of CDA obtained in the training process.
Keywords: intelligent control system, cognitive digital automata, artificial intelligence, neural networks, machine learning, cognition, Petri nets, equation of states, mathematical modeling, synthesis, generation, analysis, logic.

c 2019 European Society of Computational Methods in Sciences and Engineering

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