Тип публикации: статья из журнала
Год издания: 2024
Идентификатор DOI: 10.3390/polym16121752
Аннотация: <jats:p>This paper explores the application of multi-objective optimization techniques, including MOPSO, NSGA II, and SPEA2, to optimize the hyperparameters of artificial neural networks (ANNs) and support vector machines (SVMs) for predicting the physical properties of textile polymer composite materials (TPCMs). The optimization Показать полностьюprocess utilizes data on the physical characteristics of the constituent fibers and fabrics used to manufacture these composites. By employing optimization algorithms, we aim to enhance the predictive accuracy of the ANN and SVM models, thereby facilitating the design and development of high-performance textile polymer composites. The effectiveness of the proposed approach is demonstrated through comparative analyses and validation experiments, highlighting its potential for optimizing complex material systems.</jats:p>
Журнал: Polymers
Выпуск журнала: Т. 16, № 12
Номера страниц: 1752
ISSN журнала: 20734360
Место издания: Basel
Издатель: MDPI