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

Part 8: Reinforcement and Radial Basis Function ANNInternational audienceRaw goat milk pricing is based on the milk quality especially on fat, solid not fat (SNF) and density. Therefore, there is a need of approach for composition quantization. This study applied radial basis function network (RBFN) to calibrate fat, SNF, and density with visible and near infrared spectra (400~2500 nm). To find the optimal parameters of goal error and spread used in RBFN, a response surface method (RSM) was employed. Results showed that with the optimal parameters suggested by RSM analysis, R2 difference for training and testing data set was the smallest which indicated the model was less possible of overtraining or undertraining. The R2 for testing set was 0.9569, 0.8420 and 0.8743 for fat, SNF and density, respectively, when optimal parameters were used in RBFN

ifip advances in information and communication technology

Application of Radial Bases Function Network and Response Surface Method to Quantify Compositions of Raw Goat Milk with Visible/Near Infrared Spectroscopy