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强大的全参数控制方法:通过memristor构建多滚动HNN
Zhiqiang Wan1, Yi-Fei Pu1, Minghong Qin2
1College of Computer Science, Sichuan University, Chengdu, 610065, China.
概括
这项研究引入了一种新的记忆链霍普菲尔德神经网络 (MCHNN),可以克服现有方法的复杂性和灵敏性问题. MCHNN提供了一个更简单的设计,用于生成多种多滚动混乱信号.
科学领域:
- 非线性动力学是一种非线性动力学.
- 复杂的系统复杂的系统.
- 计算神经科学是一种神经科学.
背景情况:
- 现有的多滚动霍普菲尔德神经网络 (HNN) 对参数变化和日益复杂性的高度敏感.
- 这些局限性阻碍了多滚动HNN用于生成混乱信号的实际应用.
研究的目的:
- 为新型记忆链HNN (MCHNN) 提出一个强大的全参数控制方法.
- 通过开发更简单,更实用的多滚动吸引器生成器来解决现有HNN的局限性.
主要方法:
- 详细的理论和数值分析一个新设计的memristor的电气特性.
- 使用平衡点和稳定性分析研究MCHNN的多滚动吸引器结构.
- 通过参数和初始状态变化探索复杂的动态,包括多稳定性和吸引力转换.
主要成果:
- 拟议的MCHNN与传统的HNN相比,具有更简单的链路拓.
- MCHNN成功地产生了各种复杂动态的多滚动吸引器,包括多稳定性和轨道转换.
- 一个数字实验平台验证了MCHNN在生成可用的多滚动混乱信号方面的可行性.
结论:
- 小说MCHNN提供了一种更强大,更简单的方法来构建多滚动混乱系统.
- MCHNN产生各种混乱动态的能力及其实际实施证明了它的潜力.
- 由于MCHNN的高随机性,它对诸如伪随机数生成等应用非常有希望.
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