Towards a Fire Alarm Model Based on Variable Learning Rate Algorithm with Weight Factor
Wei Wang, Liping Yang, Lei Xu
Available Online March 2014.
- https://doi.org/10.2991/mce-14.2014.22How to use a DOI?
- fire alarm; neural network; variable learning rate; weight factor
- Fire alarm with Neural Network (NN) can learn knowledge from multiple sensor data fusion. By adjusting network weight, more stable fire alarm can be achieved. Classic BP NN is likely to fall into local minimum. To address this problem, a Variable Learning Rate Algorithm with Weight Factor (VLRA-BP) was proposed and introduced into automatic intelligent decision in fire alarm. The model with VLRA-BP algorithm uses temperature and smoke sensors to perform intelligent information transformation, so as to achieve target of timely alarming and decrease system false alarm rate. Simulation experiment result shows that system accuracy and average error can effectively monitor simulated fire scene and forecast fire.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Wei Wang AU - Liping Yang AU - Lei Xu PY - 2014/03 DA - 2014/03 TI - Towards a Fire Alarm Model Based on Variable Learning Rate Algorithm with Weight Factor BT - 2014 International Conference on Mechatronics, Control and Electronic Engineering (MCE-14) PB - Atlantis Press SP - 103 EP - 106 SN - 1951-6851 UR - https://doi.org/10.2991/mce-14.2014.22 DO - https://doi.org/10.2991/mce-14.2014.22 ID - Wang2014/03 ER -