一类大型辐射环神经网络的倾斜预测
Yunxiang Lu1, Min Xiao1, Xiaoqun Wu2
1College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
概括
本研究使用动态系统理论探索大规模神经网络中的倾斜机制. 它确定了突触延迟,反和拓作为影响网络动态和临界点的关键因素.
科学领域:
- 神经科学是一个神经科学.
- 动态系统理论 动态系统理论
- 网络科学 网络科学
背景情况:
- 了解大规模神经网络中的集体动态是具有挑战性的.
- 倾斜机制对于网络状态转换至关重要.
研究的目的:
- 应用动态系统理论和倾斜机制来分析大规模的神经网络.
- 识别影响网络动态和临界点的关键因素.
主要方法:
- 介绍了一个新的 (n+mn) 规模的辐射环神经网络.
- 采用科茨流图拓方法来导出特征方程.
- 使用基于积分元素概念的代数方法来预测临界点.
主要成果:
- 突触传输延迟可以诱导和放大周期性振荡.
- 自我反系数和网络拓影响着临界点的出现.
- 激活函数影响平衡解决方案和收速度.
结论:
- 拟议的辐射环神经网络与较小的网络相比,显示出更强大的稳定性.
- 该方法有效预测大规模神经网络中的临界点.
- 结果提供了神经网络动态在各种因素组合下的全面视图.
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