Analog Self-Learning Automata of Radio Engineering Devices Based on Cyber-Physical Networks

The article defines the principles of organizing analog self-learning automata based on cyber-physical networks and their application in radio engineering devices. A model is presented that generates parametric, structural and flow dynamics of changes in the states of a technical system. The possibilities of analog technologies are shown, which can significantly increase the efficiency of distributed control networks for the functional units of radio engineering devices. A model of three-valued sequential parametric logic is presented, which makes it possible to implement hardware algorithms based on analog automata that perform hardware functional-logical transformations of signals without switching to digital and software processing. Examples are considered that show the effectiveness of the use of analog self-learning devices for the tasks of extracting signals from interference and identifying objects. The spline signal generator shows the possibility of generating signals of a given shape in the frequency range up to tens of MHz, which makes it possible to use it in the synthesizers of stimulating signals of built-in control devices. High-speed analog memory in conjunction with a spline generator underlies the creation of self-learning automata for extracting a useful signal from noise. The presented solutions are not an alternative to digital technologies. They show new, unclaimed possibilities of analog technology, which in some cases significantly increase the efficiency and reliability of radio systems, reduce their design complexity.