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View all- Lu Z(2023)Research on Python Crawling Algorithm in Model Data Visualization2023 World Conference on Communication & Computing (WCONF)10.1109/WCONF58270.2023.10235143(1-6)Online publication date: 14-Jul-2023
Research on Data Generation Model Based on Improved SeqGAN
Authors: 
Jian Dou, Shuang Qie, Jizhe Lu, Yi RenAuthors Info & Claims
Pages 45 - 50
Abstract
With the demand of integrated energy metering business and the rise of artificial intelligence technology, the data generation model of digital equipment has become the focus of attention. As the most widely used method in the field of image generation, the implicit method based on GAN has great development potential and strong domain expansion ability. The addition of reinforcement learning method makes the GAN correlation algorithm suitable for data generation of discrete data. This paper proposes an improved SeqGAN model, reconstructs the original SeqGAN model, improves the roll-out module of the original model, uses model parameters lagging behind the generator, and increases the stability of long sequence reinforcement learning. Compared with some existing popular algorithms, the performance of the proposed model algorithm is significantly better than that of the comparison algorithm when the training times are enough (more than 150 times), which lays a foundation for its application in data generation of digital equipment.
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Published In
February 2021
325 pages
ISBN:9781450388825
DOI:10.1145/3457784
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Publication History
Published: 30 July 2021
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- Refereed limited
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- science and technology project of State Grid Corporation headquarters
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ICSCA 2021
ICSCA 2021: 2021 10th International Conference on Software and Computer Applications
February 23 - 26, 2021
Kuala Lumpur, Malaysia
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View all- Lu Z(2023)Research on Python Crawling Algorithm in Model Data Visualization2023 World Conference on Communication & Computing (WCONF)10.1109/WCONF58270.2023.10235143(1-6)Online publication date: 14-Jul-2023
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