Abstract
The objective of study is to develop a neural network algorithm for early warning of fire on a ship, using the results of measuring a multitude of sensors. The sensors are designed to measure the main fire factors such as temperature, concentration of carbon dioxide and the presence of smoke. Optimal coordinates of the sensors of the multi-sensor system were solved using suggested evolutionary algorithm. The study is based on the results of modeling fires on a supercomputer. The measurement of many fire factors allowed to develop an algorithm for determining the type of ignition. The algorithm uses a probabilistic neural network with delays at the input. Knowledge of the ignition type makes it possible to increase the speed of reaction to fire and to reduce damage on the ship. The suggested enhanced genetic algorithm fulfills optimization of the sensors position in the cabin. The algorithm allows to reduce the mean time of fire detection by of 15% in average. This effect can be achieved by increasing of the number of sensors in the cabin from 3 up to 11.
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Acknowledgment
The authors are gratitude the administration of the supercomputer center of Saint Petersburg State Polytechnic University for the opportunity to perform simulations to develop a neural network algorithm for early warning of fire on a ship, using simulation of sensors measurement results.
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Guseva, A.I., Malykhina, G.F., Nevelskiy, A.S. (2019). Neural Network Based Algorithm for the Measurements of Fire Factors Processing. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research II. NEUROINFORMATICS 2018. Studies in Computational Intelligence, vol 799. Springer, Cham. https://doi.org/10.1007/978-3-030-01328-8_17
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DOI: https://doi.org/10.1007/978-3-030-01328-8_17
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