Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (2): 628-637.DOI: 10.11772/j.issn.1001-9081.2023020196

• Frontier and comprehensive applications • Previous Articles    

Performance evaluation of industry-university-research based on statistics and adaptive ParNet

Rui ZHANG1(), Siqi SONG1, Jing HU1, Yongmei ZHANG2, Yanfeng CHAI1   

  1. 1.College of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan Shanxi 030024,China
    2.College of Information,North China University of Technology,Beijing 100144,China
  • Received:2023-02-28 Revised:2023-05-11 Accepted:2023-05-15 Online:2024-02-22 Published:2024-02-10
  • Contact: Rui ZHANG
  • About author:SONG Siqi, born in 1998, M. S. candidate. Her research interests include intelligent information processing.
    HU Jing, born in 1977, Ph. D., professor. Her research interests include intelligent optimization algorithm.
    ZHANG Yongmei, born in 1967, Ph. D., professor. Her research interests include intelligent information processing.
    CHAI Yanfeng, born in 1986, Ph. D., lecturer. His research interests include intelligent information processing.
  • Supported by:
    Ministry of Education Humanities and Social Sciences Research Project(23YJCZH299);Research Project of Graduate Education Reform in Shanxi Province(2021YJJG244);Teaching Reform and Innovation Project of Higher Institutions in Shanxi Province(J20230845);Shanxi Province Production and Education Integration Graduate Joint Training Demonstration base Project(2022JD11);Project of Joint Training Demonstration Base for Postgraduates in Taiyuan University of Science and Technology(JD2022004);Teaching Reform and Research Project of Taiyuan University of Science and Technology(JG202266)


张睿1(), 宋思琪1, 胡静1, 张永梅2, 柴艳峰1   

  1. 1.太原科技大学 计算机科学与技术学院,太原 030024
    2.北方工业大学 信息学院,北京 100144
  • 通讯作者: 张睿
  • 作者简介:宋思琪(1998—),女,山西太原人,硕士研究生,主要研究方向:智能信息处理
  • 基金资助:


The existing industry-university-research performance evaluation systems and methods have problems such as single coverage of evaluation indicators, insufficient expression of evaluation sample features, and self-optimization ability of evaluation models to be improved, the system and method of subjective and objective intelligent evaluation of industry-university-research comprehensive performance were proposed. Firstly, for the three-party cooperation subjects, the factors and the connections between these factors that affect performance in the process of industry-university-research cooperation were excavated, and the three-level subjective and objective performance evaluation system of industry-university-research was self-constructed. Secondly, the features expression of discrete samples was enhanced by mapping the collected discrete sequence evaluation samples to different high-dimensional spatial domains, such as polar coordinate space and Markov transfer matrix. Then, through the chaotic optimization strategy design based on elite reverse somersault foraging, the depth model redundancy compression and hyperparameter global optimization efficiency were improved, and the ParNet (Parallel Network) classification model with lightweight compression and high-dimensional superparameter Adaptive optimization (AParNet) was constructed. Finally, the model was applied to industry-university-research performance evaluation to achieve high-performance intelligent performance evaluation. The experimental results show that this method fits well with the applications of discrete sequence non-linear classification and improves the classification performance while reducing the computational load when an optimization strategy is added to the model. Specifically, compared to ParNet, AParNet reduces the number of parameters by 10.8%, effectively achieving model compression, and its classification accuracy in performance evaluation of industry-university-research cooperation can reach 98.6%. Therefore, in the applications of intelligent performance evaluation of industry-university-research cooperation, the proposed method improves the adaptive ability of evaluation model and achieves accurate and efficient industry-university-research performance evaluation.

Key words: performance evaluation of industry-university-research cooperation, fuzzy statistics, multi-spatial domain mapping, Convolutional Neural Network (CNN), model self-optimization strategy



关键词: 产学研合作绩效评价, 模糊统计, 多空间域映射, 卷积神经网络, 模型自优化策略

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