Prevention of emergency situations in complex technical systems using a neuromorphic approach

Abstract

The paper proposes a scheme of emergency prevention system based on neuromorphic approach. The system includes a prediction unit that implements a mathematical model of the cerebellum predictive functions, and an alarm unit that implements the pain sensation model, proposed by the authors earlier. As a basic element of the proposed system the Compartmental Spiking Neuron Model (CSNM) was used, capable of learning from a small number of examples. The use of neuromorphic approach allows to overcome the limitations associated with the formalizing complexity of the systems being diagnosed and the low availability of data for modeling the processes occurring in them. The overcoming these limitations is possible due to the possibility of learning from a small number of examples and the absence of the need to model the system being diagnosed itself. The paper also presents the results of testing of the proposed scheme, which was carried out on a computer model using synthetic data. The results of the testing showed the fundamental applicability of the proposed scheme in neuromorphic control systems.