竞争性神经网络的多稳定性和稳定性,时间延迟不同
IEEE transactions on neural networks and learning systems
|October 11, 2023
概括
本研究分析了具有时间变化的延迟的竞争性神经网络 (NN) 的多稳定性和稳定性. 新条件确保了稳定的平衡点,增强了NN稳定性理论.
科学领域:
- 计算神经科学是一种神经科学.
- 动态系统理论 动态系统理论
- 人工智能的人工智能
背景情况:
- 竞争性神经网络 (NN) 对复杂的计算至关重要.
- 了解它们的稳定性和强度,以及随时间变化的延迟是必不可少的.
- 现有的理论往往缺乏易于验证的条件.
研究的目的:
- 分析具有不同时间延迟的竞争性NNN的多稳定性和稳定性.
- 发展足够的条件,使稳定的平衡点能够共存.
- 扩展现有的NNs稳定性理论.
主要方法:
- 基于激活函数的几何结构的分析.
- 导出局部指数,功率和对数稳定性的条件.
- 对抗干扰的强度的调查.
主要成果:
- 为多个平衡点的共存建立了足够的条件.
- 这些平衡点被证明是局部指数稳定的.
- 稳定性结果包括指数式,乘数式和对数式类型.
- 在扰动下,稳定平衡点的稳定性得到证明.
结论:
- 对于NN稳定性的建议条件很容易验证.
- 这项工作丰富了竞争性NNs的稳定性理论.
- 数字示例验证了理论发现.
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