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Abstract

It is important to design an intelligent fire detection system based on the nervous system and particle optimization to find the fire time in the building. The use of intelligent methods in the fire detection system for data processing can reduce the risk of fire. In this paper, the original information of the firefighter network (FFN) is processed intelligently according to the possibility of the nervous system and the fire. In order to improve the quality of the system, the sensor data is applied to the generalized singular-value decomposition technique by analyzing the sound waves in the data. At the same time, the proposed neurological system was used for cell quality implantation new model particle mass optimization (PMO) using sushisen algorithms. In the simulation work, the data sensor is stored in the room by the network and is suitable for the target network. After that, compare the product to the traditional Multilayer Perceptron network. The simulation results represent a classic result.

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Keywords

Firefighter Network, Neural Network, Particle Mass Optimization.