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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Gas disaster is the serious threat to coal mine safety, the accurate prediction of coal mine methane (CMM) is one effective method avoiding the hazard occurrence. This paper first confirmed the chaotic characteristic of CMM sequence and calculated the delay time and embedding dimension. Combined chaotic sequential phase space reconstruction and the particle swarms optimized RBF neural network (PSO-RBFNN), build a new coupled model. The Chaotic reconstructed Time-series input PSO-RBF neural network model was proposed and highlighted its advantages by comparing the other three conventional models, Time-series input RBFNN (T-RBFNN), Chaotic reconstructed Time-series input RBFNN (CT-RBFNN) and PSO-RBFNN. The performance rank was CT-PSO- RBFNN, CT-RBFNN, T-PSO-RBFNN, T-RBFNN.

Short Term Prediction of Coal Mine Methane Concentration with Chaos PSO-RBFNN Model in Underground Coal Mines