Team of Neural Networks to Detect the Type of Ignition

Abstract

The article is about the development of a modern multisensory fire system, which has sensors for temperature, CO concentration and smoke concentration. The presence of several different types of sensors allows determine the type of source of ignition, which make possible automatically determine the means of fire extinguishing at the very beginning of the ignition process. The study was carried out on the basis of simulation results obtained in the supercomputer center. It simulated the processes of ignition in the ship’s rooms for various sources of fire: paper, household waste containing plastic, gasoline, alcohol-containing substances and electrical cables. As the study showed, a good result can be obtained with the help of a team of specially organized neural networks. A team of neural networks divided into two levels has been proposed to solve this problem. At the first level, neural networks with partial training are used. At the second level, a probabilistic neural network. The fire system is highly flexible at the hardware level because it has a wireless interface that allows quick reconfiguration. The software of the fire system, in this case also has a high flexibility, allows for simple expansion, contraction or modification of software modules in the conditions of changing sources of ignition in the room.