發(fā)布時間：2018-06-27 08:35

  本文選題：績效評價 + 粒子群優(yōu)化　； 參考：《江蘇科技大學學報(自然科學版)》2017年04期

【摘要】：客觀、公正、準確的科研績效評價是調動和提高高校及科研機構科研人員工作積極性和科技創(chuàng)新能力的重要措施.文中提出了一種基于RBF神經(jīng)網(wǎng)絡的科研績效精細評價模型,以歸一化后的科研指標數(shù)據(jù)乘以相應權系數(shù)作為網(wǎng)絡輸入,利用優(yōu)、良、中、及格和不及格5級評價作為輸出,采用粒子群優(yōu)化算法通過交叉驗證對RBF網(wǎng)絡結構參數(shù)進行了優(yōu)化.通過RBF網(wǎng)絡結構和輸入輸出特性分析發(fā)現(xiàn),訓練后的RBF網(wǎng)絡權值與5級評價結果高度相關,并較5級評價結果更能精細區(qū)別科研績效差異.該權值可直接用來進行科研績效精細評價.文中推廣了RBF網(wǎng)絡在科研績效評價中的應用,并為進行類似評價或評估工作提供了一種新思路.
[Abstract]:Objective, fair and accurate evaluation of scientific research performance is an important measure to arouse and improve the enthusiasm of scientific research personnel and the ability of scientific and technological innovation in universities and scientific research institutions. In this paper, a fine evaluation model of scientific research performance based on RBF neural network is proposed. The normalized scientific research index data multiplied by the corresponding weight coefficient are taken as the input of the network, and the five grades of excellent, good, moderate, passing and failing are used as the output. Particle swarm optimization (PSO) algorithm is used to optimize the parameters of RBF network structure. Through the analysis of RBF network structure and input and output characteristics, it is found that the weight value of RBF network after training is highly correlated with the evaluation results of level 5, and it can distinguish the difference of scientific research performance more carefully than the results of evaluation of level 5. The weight value can be directly used for fine evaluation of scientific research performance. In this paper, the application of RBF neural network in evaluation of scientific research performance is extended, and a new idea is provided for similar evaluation or evaluation.
【作者單位】： 江蘇科技大學經(jīng)濟管理學院;
【基金】：國家自然科學基金青年項目(71303096) 2016年校研究生教育教學改革研究與實踐項目(YJG2016Y_14)
【分類號】：G311;TP183
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本文編號：2073328