Research on the Evaluation of Teaching Quality Based on CGSAB

DOI

10.2991/icemse-16.2016.80How to use a DOI?

Keywords

Teaching quality, evaluation, gravitational search algorithm, chaotic series, BP neural network

Abstract

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.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 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  - 10.2991/icemse-16.2016.80
ID  - Cheng2016/12
ER  -