一种人工神经网络方法用于语言学习模型
Zulqurnain Sabir1, Salem Ben Said2, Qasem Al-Mdallal3
1Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.
Scientific reports
|December 20, 2023
概括
这项研究介绍了一种人工智能 (AI) 方法,使用尺度对联梯度神经网络 (SCJGNN) 来解决基于语言的差异模型. 人工智能方法准确地模拟语言学习阶段,错误最小.
科学领域:
- 计算语言学 计算语言学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 基于语言的差异模型对于理解学习过程至关重要.
- 为这些模型开发精确的数值解决方案在计算上具有挑战性.
- 现有的方法在捕捉学习动态时可能缺乏效率或精度.
研究的目的:
- 用人工智能呈现基于语言的差分模型的数值解决方案.
- 实施和验证一个尺度联梯度神经网络 (SCJGNN) 程序.
- 将语言学习分为未知的,熟悉的和掌握的阶段.
主要方法:
- 采用了一种基于尺度结合梯度神经网络 (SCJGNN) 的人工智能 (AI) 程序.
- 采用亚当方案来最大限度地减少数据集概括的平均平方误差.
- 配置了SCJGNN具有日志-西格激活功能,12个神经元和特定层结构,在训练 (75%),验证 (13%) 和测试 (12%) 中处理数据的比率.
主要成果:
- 在数值解中达到高精度,绝对误差范围为所有学习类的10-06到10-08.
- 通过每个学习阶段的回归分析证明了完美的模型性能.
- 使用直方图和功能适应性分析验证了SCJGNN方法的可靠性.
结论:
- 基于人工智能的SCJGNN为解决基于语言的差分模型提供了强大而准确的方法.
- 该模型有效地区分了未知的,熟悉的和掌握的语言学习状态.
- 该SCJGNN方法显示了计算机语言学和教育技术应用的巨大潜力.
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