适应梯度神经网络的加速方法,用于解决时间依赖的线性方程:由状态触发的视角
IEEE transactions on neural networks and learning systems
|March 14, 2024
概括
一种新的混合状态触发离散 (HSTD) 增强了适应梯度神经网络,用于解决时间依赖的线性方程. 这种方法提高了加速性能和计算效率,在机器人应用中得到了验证.
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:
- 适应梯度神经网络 (AGNNs) 用于解决时间依赖的线性方程 (TDLEs).
- 现有的AGNN加速方法通常依赖于激活函数或时间变化的系数.
- 需要改进的加速策略,考虑到可变的采样周期和系统动态.
研究的目的:
- 为AGNN引入一种新的加速技术,即混合状态触发离散 (HSTD),用于AGNN.
- 提高解决TDLEs的加速性能和计算效率.
- 为了解决AGNNs当前加速方法的局限性.
主要方法:
- 拟议的HSTD整合了两个组成部分:自适应采样间隔状态触发离谱化 (ASISTD) 和自适应系数状态触发离谱化 (ACSTD).
- ASISTD解决了与可变采样周期相关的挑战.
- ACSTD通过分析利亚普诺夫函数的进化动态来确定系数.
主要成果:
- 数字模拟表明,与传统的离散化方法相比,HSTD显著提高了加速性能.
- 该HSTD方法提供了显著的计算优势.
- 通过机器人技术的应用来验证HSTD的有效性.
结论:
- 拟议的HSTD是加速AGNN解决TDLEs的有效方法.
- HSTD提供了卓越的加速性能和计算效率.
- 基于控制理论的HSTD设计为增强神经网络性能提供了一个新的视角.
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