articles

Neural network architecture based on polymer macrocells acting as neural elements and capable to generate electrically conductive synaptic junctions is developed. The architecture of a neural network processor where each component can record and produce a response to energy effects of different nature: electrical, optical and electrostatic has been developed. This function of the neural network eliminates the limitations related to the perception of the input information only by the input layer of the neural network processor. The impact of the influence of different input data representations on the information processing by the macrocell has been studied. The efficiency of temporal and electrical degradation of a neural network macrocell as a complex phenomenon directed on adaptation of a neural network to this process and generation of an algorithm for distribution of elements of a tutorial sample evenly over the total volume of available neural cells has been studied. The article studies a method of generation of episodic circulating memory, which significantly improves the speed of generation of a solution by the network, taking into account the gravitational interaction with macrocells. The revealed specific nature of the influence of the gravitational field on the functioning of neural network clusters was the basis for natural selection of cells by the performed properties depending on the spatial position in the neural network architecture. A method of exposure of the cell to physical equivalents of tutorial sample elements converted into different energy patterns to control the configuration of the macrocell using all available modes of information perception has been provided. Each exposure containing information on the tutorial element (optical and electrostatic) supports the generated generalising abilities of the neural macrocells composition. Practical significance of the performed research comprises the designed neural network system, which achieves an increase in performance due to self-organising oscillating neural clusters. The developed neural network model is used to reduce radio deviation and associated navigation errors due to conductive obstacles or atmospheric formations.