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

For a high energy loss and complex system of ball mill, this paper provide a ball mill load detection method based on genetic algorithm optimizing BP neural network. The effective frequency range of mill sound signal is analyzed. The soft measurement model of mill load based on mill sound signal is built. In order to solve the problem which converge slowly and easily reach minimal value, the global optimization of GA (genetic algorithm) local optimization of BP neural network will be combined to improve the BP neural network. Compare with the detected mill load error generated from existing BP neural network and RBF neural network based on K-means. The experiments results show that the proposed algorithm has better precision.

The Ball Mill Load Measuring algorithm though Grinding tone signal based on GA