有效的预定义时间自适应神经网络用于计算时间变量张量摩尔-罗斯逆向.
IEEE transactions on neural networks and learning systems
|January 30, 2024
概括
新的预定义时间自适应神经网络 (PTANN) 模型有效计算张量摩尔-罗斯反向. 事件触发PTANN (ET-PTANN) 进一步降低了计算,提高了诸如声音源本地化等应用程序的效率.
科学领域:
- 计算数学 计算数学 计算数学
- 人工智能的人工智能
- 控制理论 控制理论
背景情况:
- 时间变量张量摩尔-罗斯 (MP) 逆的高效计算对于各种应用至关重要.
- 现有的方法经常面临计算资源分配和效率方面的挑战.
- 神经网络方法具有潜力,但需要优化速度和资源管理.
研究的目的:
- 提出新的预定义时间自适应神经网络 (PTANN) 和事件触发的PTANN (ET-PTANN) 模型.
- 为了实现强烈的预定义时间收计算时间变量张量MP反向.
- 与传统方法相比,提高计算效率和资源分配.
主要方法:
- 开发具有新型适应参数和激活功能的PTANN模型.
- 将事件触发机制集成到PTANN模型中,以创建ET-PTANN模型.
- 数学推导收时间界限和事件触发间隔.
- 模拟和基于应用程序的验证.
主要成果:
- PTANN模型实现了强大的预定义时间收,适应参数与误差规范成比例.
- 通过事件触发,ET-PTANN模型通过调整步骤大小和减少计算频率来进一步提高效率.
- 数学推导证实了收性质和最佳事件触发间隔.
- 模拟显示PTANN和ET-PTANN模型在效率和趋同率方面优于现有方法.
结论:
- 拟议的PTANN和ET-PTANN模型为时间变量张量MP反向提供了显著的计算效率和融合速度的改进.
- 在PTANN中的自适应参数策略有效地分配计算资源.
- ET-PTANN中的事件触发机制通过减少计算负载进一步优化了性能.
- 这些模型在现实应用中展示了实际的实用性,例如移动音源本地化.
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