English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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. Keywordsfire alarm; neural network; variable learning rate; weight factor

Towards a Fire Alarm Model Based on Variable Learning Rate Algorithm with Weight Factor