Department of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China.
Department of Automation, Beijing Information Science and Technology University, Beijing 100192, China.
Comput Intell Neurosci. 2022 May 24;2022:9564443. doi: 10.1155/2022/9564443. eCollection 2022.
This study exploits a novel enhanced genetic neural network algorithm with link switches (EGA-NNLS) to model the professional university course evaluating system. Various indices should be employed to evaluate the learning effect of a professional course comprehensively and objectively, and the traditional artificial evaluation methods cannot achieve this goal. The presented data-driven modeling method, EGA-NNLS, combines a neural network with link switches (NN-LS) with an enhanced genetic algorithm (EGA) and the Levenberg-Marquardt (LM) algorithm. It employs an optimized network structure combined with EGA and NN-LS to learn the relationships between the system's input and output from historical data and uses the network's gradient information via the LM algorithm. Compared with the traditional backpropagation neural network (BPNN), EGA-NNLS achieves a faster convergence speed and higher evaluation precision. In order to verify the efficiency of EGA-NNLS, it is applied to a collection of experimental data for modeling the professional university course evaluating system.
本研究利用一种具有链路开关的新型增强遗传神经网络算法（EGA-NNLS）来对专业大学课程评估系统进行建模。为了全面、客观地评估一门专业课程的学习效果，需要使用各种指标，而传统的人工评估方法无法实现这一目标。本文提出的数据驱动建模方法 EGA-NNLS 将具有链路开关的神经网络（NN-LS）与增强遗传算法（EGA）和列文伯格-马夸尔特（LM）算法相结合。它采用优化的网络结构与 EGA 和 NN-LS 相结合，从历史数据中学习系统输入和输出之间的关系，并利用网络的梯度信息通过 LM 算法。与传统的反向传播神经网络（BPNN）相比，EGA-NNLS 具有更快的收敛速度和更高的评估精度。为了验证 EGA-NNLS 的效率，将其应用于一组实验数据以对专业大学课程评估系统进行建模。
