Hebei Normal University Huihua College, Shijiazhuang, Hebei 050091, China.
Comput Intell Neurosci. 2022 Jul 13;2022:2825530. doi: 10.1155/2022/2825530. eCollection 2022.
With the development of the times, education presents a new trend, but the teaching characteristics of dance classroom teaching cannot adapt to the current development trend. In this article, the author analyzes modern information technology, hoping to realize the teaching of folk dance on the Internet and provide a new model of online distance teaching for folk dance teaching. The author analyzes the current teaching problems in colleges and universities, and proposes to change the existing teaching situation based on dynamic process neural network model identification and artificial intelligence, and instead use online remote network ethnic dance teaching. Online distance education can enable flexible teaching of folk-dance courses, deeply dig into the theoretical basis of distance teaching, and use online distance network teaching to make teaching time more flexible, not only providing new teaching methods but also introducing new teaching concepts. Based on the traditional neural network model identification, a dynamic process neural network model identification is developed. This model is no longer subject to the input limitation of the traditional neural network model, the processing time is relaxed, and the advantages are more obvious. In this research, the author introduces dynamic process neural network model identification in time series data mining, and makes full use of artificial intelligence to deeply analyze the classification and prediction problems in the context of time series.
随着时代的发展，教育呈现出新的趋势，但舞蹈课堂教学的教学特点却无法适应当前的发展趋势。本文作者分析了现代信息技术，希望能在互联网上实现民间舞教学，并为民间舞教学提供一种新的在线远程教学模式。作者分析了目前高校的教学问题，提出基于动态过程神经网络模型识别和人工智能改变现有教学状况，转而采用在线远程网络民族舞教学。在线远程教育可以使民间舞蹈课程的教学更加灵活，深入挖掘远程教学的理论基础，利用在线远程网络教学使教学时间更加灵活，不仅提供新的教学方法，还引入新的教学理念。在传统神经网络模型识别的基础上，开发了动态过程神经网络模型识别。该模型不再受传统神经网络模型输入限制，放宽了处理时间，优势更加明显。在这项研究中，作者将动态过程神经网络模型识别引入到时间序列数据挖掘中，并充分利用人工智能深入分析时间序列背景下的分类和预测问题。
