期刊
CHINESE JOURNAL OF ELECTRONICS
卷 30, 期 1, 页码 92-101出版社
WILEY
DOI: 10.1049/cje.2020.12.005
关键词
Big data; Particle swarm optimization; LSTM neural network; Trust model
资金
- National Natural Science Foundation of China [61572260, 61872196, 61872194, 61402241]
- Scientific & Technological Support Project of Jiangsu Province [BE2017166]
- Jiangsu Natural Science Foundation for Excellent Young Scholar [BK20160089]
- Research of Natural Science of NJUPT [NY217050]
- Jiangsu Government Scholarship for Overseas Studies
This paper proposes a trust evaluation model based on LSTM, which utilizes its powerful learning ability and dynamic timing, and combines PSO algorithm to solve the weight and threshold initialization issue, improving the accuracy of trust evaluation in big data environments.
Due to the challenge of increasing data volume, the traditional trust model is unable to manage data with high efficiency and effectively extract useful information hidden in big data. To fully utilize big data and combine machine learning with trust evaluation, a trust evaluation model based on Long short-term memory (LSTM) is presented. The powerful learning ability, expressive ability and dynamic timing of LSTM can be applied to study data while avoiding the vanishing and exploding gradient phenomena of traditional Recurrent neural networks (RNNs) to ensure that the model can learn sequences of random length and provide accurate trust evaluation. Targeting the performance instability caused by the LSTM model's random initialization of weights and thresholds, Particle swarm optimization (PSO), one of the intelligent algorithms, is introduced to find global optimal initial weights and thresholds. Experiments proved that the trust model proposed in this paper has high accuracy and contributes a new idea for trust evaluation in big data environments.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据