具有和冲动输入的随机神经网络的平均平方指数稳定分析
Hao Deng1, Chuandong Li1, Fei Chang1
1Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, PR China.
概括
这项研究研究了以和冲动输入为基础的随机神经网络的指数稳定. 我们开发了方法来确保稳定性,并估计这些复杂系统的吸引力领域.
科学领域:
- 控制理论 控制理论
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 随机神经网络对于建模复杂系统至关重要.
- 冲动输入和和在网络稳定方面带来了重大挑战.
- 在这些条件下了解网络动态对于可靠的应用程序至关重要.
研究的目的:
- 为了研究具有和冲动输入的随机神经网络的指数稳定.
- 开发各种冲动序列下的稳定性的理论条件.
- 估计和优化这些网络的吸引力域.
主要方法:
- 多面体表示方法来处理和术语.
- 基于平均冲动间隔,冲动密度和模式依赖的冲动密度的稳定性条件的分析.
- 使用圆体和多面体对吸引域的估计.
- 凸起式优化以获得最佳的吸引力域.
主要成果:
- 获得了足够的条件来实现指数稳定.
- 提出了有效的方法来估计吸引力的领域.
- 多面体表示有效地解决了输入和.
- 一个三维连续时间的霍普菲尔德神经网络示例验证了结果.
结论:
- 提出的方法确保了具有和冲动输入的随机神经网络的指数稳定.
- 这些技术为分析和设计稳定的神经网络系统提供了强大的框架.
- 通过实用的神经网络示例验证了结果,证明了它们的有效性.
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