DEVICE MORPHISMS IN MARKOV NETWORK. Part 1

The article presents the results of introducing objects – device processors designated by words – into the categorical and network сNnet-scheme of the Markov algorithm and the synthesis of their morphisms. The applied (technical, technological) goal of defining the processors of devices in the composition of this algorithm is the synthesis of such a scheme that would be most suitable for use in Markov networks with abnormal inference, implemented as separate hardware devices (calculators). These networks are distinguished by the presence of an output that cannot be considered normal according to Markov, such an output is called “abnormal”. The implementation of such an output in hardware devices, in particular, processors, is of scientific and applied interest, since it allows implementing a new method of parallel and distributed computing by managing a list of data sources, in fact, a new technology. The “classical”, intuitionistic creativity according to Brouwer in Markov networks of indirect propagation is considered, some principles of its implementation are defined. To study the points of application of creativity to the Markov algorithm, in addition to non-material (mathematical, artificial, virtual) objects, objects of the world around us are taken into account, in particular, processors and some other devices that ensure the execution of the algorithm's instructions. The scientific goal of joint consideration of such objects is to find a way to synthesize Markov networks with abnormal inference capable of processing “all” morphisms, in other words, to synthesize a complete (in the mathematical sense) list of morphisms. The conducted research of creativity in such networks is also aimed at finding new, previously unaccounted for morphisms, generalization and classification of which is a constructive clarification of the methods of interaction of objects. The ultimate goal of synthesizing a complete list of morphisms is to use Markov networks with abnormal inference together with artificial intelligence. Therefore, the study regularly introduces various aspects of their interaction, otherwise – the morphism from the network to artificial intelligence and the inverse morphism are specified. The first part of the study will show the results of determining the following processors in the сNnet-scheme of the Markov algorithm: control channel, input-output, small and large correlators. Conclusions are made that the obtained сNnet-scheme of the Markov algorithm with small correlators can be used in telecommunications, in particular, for processing data from digital communication channels in conditions of interference.