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

Polynomial regression (PR) has been widely used to model simulations and experimental engineering data. The regression relationship of engineering data often varies greatly in different sub-sample spaces, making it difficult to evaluate accurately using a unified PR model. To solve this issue, a decision tree-assisted polynomial regression (DTPR) model is proposed in this article, in which a new decision tree is designed to partition the sample space according to the regression relationship. To make a prediction for a new sample, it is first compared from the root node of the decision tree until it reaches a leaf node; then, the output is obtained through the PR model of the leaf node. The experimental results indicate that the proposed model can show competitive performance in both mathematical functions and engineering cases. The proposed model is applied to the cutting force analysis of cutters of a tunnel boring machine to demonstrate its advantages.

A decision tree-assisted polynomial regression model with application in the cutting force analysis of cutters of a tunnel boring machine