Accuracy and Precision Analysis of the Graph Complexity Connectivity Method

Abstract

For a given electro-mechanical product, represented using assembly models and function structures, the assembly time (AT) and market value (MV) are influenced by complexity of the product. Given the AT and MV of a set of known products, complexity values can be used to predicted AT and MV for a set of unknown products using an Artificial Neural Network. This paper presents a precision analysis of four prediction models that are a combination of the aforementioned design representations and AT and MV. A sensitivity analysis of the complexity metrics was done using Multiple Linear Regression, and a set of significant metrics was identified. Lastly, a comparison of accuracy and precision for the four prediction models obtained using this set of sensitivity analysis is presented.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 15 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.