基于统计和自适应ParNet的产学研绩效评价

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

针对现有产学研绩效评价体系及方法中存在的评价指标覆盖范围单一、评价样本特征表达不充分、评价模型自优化能力待提高的问题,提出主客观产学研综合绩效智能评价的评价体系及方法。首先,围绕三方合作主体,挖掘产学研合作过程中影响绩效的要素及这些要素之间的联系,自主构建主客观产学研绩效三级评价体系;其次,通过将收集到的离散序列评价样本映射至极坐标空间、马尔可夫转移矩阵等不同高维空间域,增强离散样本特征表征;然后,通过基于精英反向翻筋斗觅食的混沌优化策略设计,提高深度模型冗余压缩和超参数的全局寻优效率,构建轻量压缩及高维超参数的自适应寻优的ParNet(AParNet)分类模型;最后,将模型应用于产学研绩效评价中,实现高性能的绩效智能评价。实验结果表明,所提方法很好地贴合了离散序列非线性分类应用,同时模型中加入优化策略后,在减少计算量的同时提高了分类性能,具体体现在:与ParNet相比,AParNet中的参数量减少了10.8%,较好地实现了模型的压缩,且它在产学研绩效评价中的分类准确率可达到98.6%。在产学研绩效智能评价应用中,该方法提高了评价模型的自适应能力,能够实现准确、高效的产学研绩效评价。

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.