Application of technology maturity assessment and achievement selection based on LSTM time series prediction

Xizhou Du; Yan Chen; Yong Li

doi:10.1117/12.3011768

7 December 2023 Application of technology maturity assessment and achievement selection based on LSTM time series prediction

Xizhou Du, Yan Chen, Yong Li

Author Affiliations +

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129415A (2023) https://doi.org/10.1117/12.3011768
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

In order to understand the application of technology maturity evaluation and achievement selection of LSTM time series prediction, research on technology maturity evaluation and achievement selection application based on LSTM time series prediction is put forward. In this paper, firstly, the evaluation index is extracted from patent data, and the data is preprocessed by high-order polynomial curve fitting model. Then, the LSTM algorithm is used to establish a combination evaluation model and a time series prediction model for technology maturity. Finally, the technology maturity evaluation and prediction results are made based on the evaluation model and the prediction data. Taking the spacecraft electric propulsion technology as an example, the results show that the percentage root mean square error performance of LSTM algorithm is better than that of SARIMA model, which verifies the effectiveness of the technology maturity evaluation and prediction method.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xizhou Du, Yan Chen, and Yong Li "Application of technology maturity assessment and achievement selection based on LSTM time series prediction", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129415A (7 December 2023); https://doi.org/10.1117/12.3011768

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