一个新的卡普托分数模型英语语言学习:分析和模拟与贝叶斯规范化方法的贝叶斯规范化方法
Maria1, Aqsa Zafar Abbasi2, Muhammad Asif Zahoor Raja3
1Department of Foreign Languages and Applied Linguistics, Yuan Ze University, 135 Yuan-Tung Road, Chung Li 32003, Taiwan.
MethodsX
|June 12, 2025
概括
本研究介绍了一种新的离散分数模型,用于英语语言学习动态. 机器学习,特别是贝叶斯规范化人工神经网络,增强了语言学习的分析和预测.
科学领域:
- 应用数学 应用数学 应用数学
- 计算语言学 计算语言学
- 机器学习 机器学习
背景情况:
- 语言学习是一个复杂的过程,从数学建模中受益.
- 分数计算为建模动态系统提供了先进的工具.
- 整合计算方法可以改善学习行为分析.
研究的目的:
- 为英语语言学习引入新的卡普托离散分数模型.
- 应用机器学习技术来估计和分析语言学习动态.
- 验证拟议模型的准确性和稳定性.
主要方法:
- 为语言学习开发一个离散的卡普托分数模型.
- 使用贝叶斯规范化人工神经网络 (BRA-NN) 作为计算解决方案.
- 分数顺序英语语言数学模型 (FOELMM) 的六个分数顺序变体的推导和分析.
主要成果:
- 提出的离散分数模型有效地捕捉了英语语言学习的动态.
- BRA-NNs为学习过程提供了准确和稳定的数值模拟.
- 与分数顺序Lotka-Volterra方法的验证证实了该模型的可靠性.
结论:
- 离散微积分和机器学习的整合提供了一种强大的方法来研究语言学习.
- 开发的模型和计算解决方案为分析学习行为提供了强大的框架.
- 这项工作为进一步研究计算语言学习模型奠定了基础.
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