Research on the Evaluation of Teaching Quality Based on CGSAB
Jiatang Cheng, Yan Xiong
Available Online December 2016.
- https://doi.org/10.2991/icemse-16.2016.80How to use a DOI?
- Teaching quality, evaluation, gravitational search algorithm, chaotic series, BP neural network
- Teaching quality evaluation is an important work in teaching management. In order to improve the accuracy of teaching quality evaluation, according to the evaluation data at a certain university, an evaluation model based on BP neural network optimized by gravitational search algorithm (GSABP) is proposed. For the GSA algorithm is easy to fall into the local optimal, the ergodicity of chaotic sequence is used to generate the initial population of GSA, and then the chaotic gravitational search algorithm (CGSA) is presented. The experimental results show that, compared with BP neural network and GSABP algorithm, the model using CGSABP has high credibility and strong generalization ability, which provides a feasible method for the accurate evaluation of teaching quality.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jiatang Cheng AU - Yan Xiong PY - 2016/12 DA - 2016/12 TI - Research on the Evaluation of Teaching Quality Based on CGSAB BT - 2016 International Conference on Education, Management Science and Economics PB - Atlantis Press SP - 318 EP - 320 SN - 2352-5398 UR - https://doi.org/10.2991/icemse-16.2016.80 DO - https://doi.org/10.2991/icemse-16.2016.80 ID - Cheng2016/12 ER -