@inproceedings{dac195de40214ea79b845ccd1602f4b8,
title = "Temperature analysis of electronic devices for reliability design based on process neural network",
abstract = "Aiming at reliability design for electronic devices, process neural networks are proposed to predict the temperature of electronic devices. To avoid errors caused by discrete input data fitting or difference, only discrete data is used when solving orthogonal transformation coefficients. To accelerate the learning speed of the gradient descent algorithm, a parameter- independent adaptive learning algorithm is developed. The results show that this model has better accuracy and generalization ability compared with artificial neural networks and linear regression method, and the parameter-independent adaptive learning algorithm has quicker convergence rate compared with parameter-fixed algorithm and adaptive learning algorithm.",
keywords = "Adaptive learning rate, Discrete process neural network, Gradient descent algorithm, Orthogonal function basis, Reliability design",
author = "Chen Ding and Zhiling Niu and Muchun Yu and Zijun Zhang and Bingwei Li",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019 ; Conference date: 15-08-2019 Through 17-08-2019",
year = "2019",
month = aug,
doi = "10.1109/SDPC.2019.00201",
language = "English",
series = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1057--1061",
editor = "Chuan Li and Shaohui Zhang and Jianyu Long and Diego Cabrera and Ping Ding",
booktitle = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
address = "United States",
}