Skip to main content

Advertisement

Complex product network change prediction method based on GANs with small sample data

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Complex product network change prediction can significantly reduce product redesign time. The accuracy of change predictions often depends on the richness of the historical sample data of this product. Aiming at the problems of less change data and low prediction accuracy in product design change prediction, this paper proposes an improved generative adversarial network using multiple discriminators based on real historical data and generated data called sdrgGAN to improve the data generation. Generated data and real historical data both are applied in the product intensity prediction. The proposed sdrgGAN has improved the multi-feature fusion ability based on feature similarity. The discriminator part is composed of several discriminators. This design improves the discriminant ability of the discriminator and is helpful in generating more realistic data. In the experiment, real historical change data of a TV set is applied. Convolutional Neural Network (CNN) and Long Short Memory Model (LSTM) are used for the prediction of a product change intensity. The experimental results demonstrate the effectiveness of the designed GANs in dealing with small sample problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Huang T, Fildes R, Soopramanien D (2019) Forecasting retailer product sales in the presence of structural change. Eur J Oper Res 279(2):459–470. https://doi.org/10.1016/j.ejor.2019.06.011

    Article  MATH  Google Scholar 

  2. Liu F, Dai Y (2023) Product quality prediction method in small sample data environment. Adv Eng Inform 56:101975. https://doi.org/10.1016/j.aei.2023.101975

    Article  MATH  Google Scholar 

  3. Li R, Yang N, Zhang Y, Liu H (2020) Risk pro-pagation and mitigation of design change for co-mplex product development (CPD) projects based on multilayer network theory. Comput Ind Eng 142:106370. https://doi.org/10.1016/j.cie.2020.106370

    Article  MATH  Google Scholar 

  4. Chen L, Zheng Y, Xi J, Li S (2020) An analysis method for change propagation based on product feature network. Res Eng Des 31(4):491–503. https://doi.org/10.1007/s00163-020-00344-7

    Article  MATH  Google Scholar 

  5. Yin LL, Sun Q, Xu YX, Shao L, Tang DB (2021) Intelligent optimization of complex product change propagation paths. J Comput Inf Sci Eng 21(4):041003. https://doi.org/10.1115/1.4048812

    Article  MATH  Google Scholar 

  6. Mahmoud M, Patrice L, Marc Z, Mohamed H (2017) Engineering Change Management: a novel approach for dependency identification and change propagation for product redesign. IFAC PapersOn-Line 50(1):12410–12415. https://doi.org/10.1016/j.ifacol.2017.08.2427

    Article  MATH  Google Scholar 

  7. Han C, Ma T, Huyan J, Tong Z, Yang H, Yang Y (2024) Multi-stage generative adversarial networks for generating pavement crack images. Eng Appl Artif Intell 131:107767. https://doi.org/10.1016/j.engappai.2023.107767

    Article  MATH  Google Scholar 

  8. Lavanya P, Singh RP, Kumaran U, Kumar P (2024) Gradient boosting classifier performance evaluation using Generative Adversarial Networks. Procedia Comput Sci 235:3016–3024. https://doi.org/10.1016/j.procs.2024.04.285

    Article  MATH  Google Scholar 

  9. He Z, Zhou W (2024) Development of machine learning-based burst capacity models for pipelines containing dent-gouges with synthetic full-scale burst test data generated using tabular generative adversarial network. Eng Appl Artif Intell 133(Part A) 108090. https://doi.org/10.1016/j.engappai.2024.108090

    Article  MATH  Google Scholar 

  10. Ren Z, Huang K, Zhu Y, Feng K, Liu Z, Fu H, Hong J, Glowacz A (2024) Progressive generative adversarial network for generating high-dimensional and wide-frequency signals in intelligent fault diagnosis. Eng Appl Artif Intell 133(Part E) 108332. https://doi.org/10.1016/j.engappai.2024.108332

    Article  Google Scholar 

  11. Zhu X, Ye X (2024) GAN-BodyPose: real-time 3D human body pose data key point detection and quality assessment assisted by generative adversarial network. Image Vis Comput 105144. https://doi.org/10.1016/j.imavis.2024.105144

  12. Lupión M, Cruciani F, Cleland I, Nugent C, Ortigosa PM (2024) Data augmentation for human activity recognition with generative adversarial networks. IEEE J Biomedical Health Inf 28(4):2350–2361. https://doi.org/10.1109/JBHI.2024.3364910

    Article  Google Scholar 

  13. Chen Q, Ye A, Zhang Y et al (2024) Information-minimizing generative adversarial network for fair generation and classification. Neural Process Lett 56:36. https://doi.org/10.1007/s11063-024-11457-8

    Article  MATH  Google Scholar 

  14. Huang S, Lei K (2020) IGAN-IDS: an imbalanc-ed generative adversarial network towards intrusion detection system in ad-hoc networks. Ad Hoc Netw 105(8):350–368. https://doi.org/10.1016/j.adhoc.2020.102177

    Article  Google Scholar 

  15. Li R, Yang N, Yi H, Jin N (2023) The robustness of complex product development projects under design change risk propagation with gray attack information. Reliab Eng Syst Saf 235:109248. https://doi.org/10.1016/j.ress.2023.109248

    Article  MATH  Google Scholar 

  16. Dong C, Yang Y, Chen Q, Wu Z (2022) A complex network-based response method for changes in customer requirements for design processes of complex mechanical products. Expert Syst Appl 199:117124. https://doi.org/10.1016/j.eswa.2022.117124

    Article  MATH  Google Scholar 

  17. Guo Y (2021) Towards the efficient generation of variant design in product development networks: network nodes importance based product configure-ation evaluation approach. J Intell Manuf 34(2):615–631. https://doi.org/10.1007/s10845-021-01813-z

    Article  MATH  Google Scholar 

  18. Zheng R, Liu M, Zhang Y, Wang Y, Zhong T (2024) An optimization method based on improved ant colony algorithm for complex product change propagation path. Intell Syst Appl 23:200412. https://doi.org/10.1016/j.iswa.2024.200412

    Article  MATH  Google Scholar 

  19. Shang J, Yang B, Ma N et al (2021) Correlation based analysis of parameter change propagation in variant product design. Int J Precis Eng Manuf 22(4):599–619. https://doi.org/10.1007/s12541-021-00473-6

    Article  MATH  Google Scholar 

  20. Li R, Yi H, Cao H (2022) Towards understandin-g dynamic design change propagation in complex product development via complex network approa-ch. Int J Prod Res 60(9):2733–2752. https://doi.org/10.1080/00207543.2021.1901155

    Article  MATH  Google Scholar 

  21. Li T et al (2022) Design change propagation ro-uting in the modular product. Adv Eng Inform 54:101784. https://doi.org/10.1016/j.aei.2022.101784

    Article  Google Scholar 

  22. Wang X, Liu X, Bai Y (2024) Prediction of the temperature of diesel engine oil in railroad locomotives using compressed information-based data fusion method with attention-enhanced CNN-LSTM. Appl Energy 367:123357. https://doi.org/10.1016/j.apenergy.2024.123357

    Article  Google Scholar 

  23. Efron B, Hastie T (2023) Computer age statistic-Al Inference: algorithms, evidence and data scie-nce. Student ed Technometrics 65(4):611–613. https://doi.org/10.1080/00401706.2023.2262897

    Article  MATH  Google Scholar 

Download references

Acknowledgements

The study was supported by Open Fund of National Key Laboratory of Intelligent Coal Mining and Rock Control SKLIS202406 and National Natural Science Foundation of China 62371451.

Author information

Authors and Affiliations

Authors

Contributions

All the authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Hongmei Wang. The first draft of the manuscript was written by Shuo Liu, and all the authors commented on previous versions of the manuscript. All the authors read and approved the final manuscript.

Corresponding author

Correspondence to Faguang Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this study.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, H., Liu, S., Zhang, S. et al. Complex product network change prediction method based on GANs with small sample data. Appl Intell 55, 249 (2025). https://doi.org/10.1007/s10489-024-06108-3

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10489-024-06108-3

Keywords