Intelligent Evaluation of Product Form Design Based on Deep Neural Network Model

Shaoyun Wang, Qiang Wu and Ning Cao

Mathematical Problems in Engineering, 2022, vol. 2022, 1-7

Abstract: In order to evaluate the product morphological design, a method of applying BP neural network to evaluate the product morphological design is proposed based on the analysis of the principle of artificial neural network. In such method, the advantages of the BP neural network with self-learning, self-organization, self-adaptation, and nonlinear dynamic processing are applied to effectively evaluate the product morphological design. Specifically, 13 product morphological design solutions of the automotive evaluation data set are selected as samples, 15 out of 18 solutions are used to train the evaluation system, and the remaining 3 solutions are used to validate the trained system. The validation results show that the relative errors between the simulated and actual values are 3.6%, −1.7%, and 2.8%, respectively. Such results also show high accuracy and simultaneously can reflect the effectiveness of our proposed system for evaluating the design solutions.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:3140489

DOI: 10.1155/2022/3140489

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