基于樽海鞘算法优化神经网络的艺术教学评价研究

Research on the Evaluation of Art Teaching Based on Neural Network Improved by Salp Swarm Algorithm

为了提高艺术设计类专业教学质量,激发老师的教学积极性以及学生学习动力和热情,提高学生的能力和素质,从课堂实验教学评价、项目创作与研究实践评价、实习(写生、采风)评价和毕业设计(论文)评价4个方面建立了一套艺术设计类专业教学质量评价体系。针对BP神经网络的预测性能受其初始权值和阈值选择的影响,樽海鞘群算法优化BP模型的初始权值和阈值,提出了一种基于SSA-BP的艺术设计类专业教学质量评价模型。与RBF和BP对比发现,SSA-BP可以有效提高艺术设计类专业教学质量评价的效果,为艺术设计类专业教学质量评价提供了新的参考。

In order to improve the teaching quality of art and design majors,stimulate teachers’enthusiasm in teaching,students’motivation and enthusiasm in learning,so as to improve students’ability and quality,a set of teaching evaluation of art and design majors has been established from four aspects:classroom experiment teaching evaluation,project creation and research practice evaluation,practice(sketching,collecting style)evaluation and graduation design(thesis)evaluation system.Considering that the prediction performance of BP neural network is affected by the selection of its initial weights and thresholds,Salp Swarm Algorithm(SSA)is used to optimize the initial weights and thresholds of BP model,and a teaching quality evaluation model of art design specialty is put forward based on SSA-BP.Compared with RBF and BP,SSA-BP can effectively improve the effect of teaching quality evaluation of art design specialty,and provide a new method for teaching quality evaluation of art design specialty.