不同质的共存吸引器,大规模的振幅控制和有限时间同步的中心循环记忆神经网络
1School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, China.
概括
这项研究介绍了用于人工智能的中央循环记忆神经网络 (CCMNN). CCMNN表现出复杂的动态,并实现了安全通信应用程序的自适应同步控制.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 复杂的系统复杂的系统.
背景情况:
- 记忆器提供类似于脑突触的记忆能力和非线性,这对人工智能进步至关重要.
- 霍普菲尔德神经网络是类似大脑的计算模型的基础.
- 混乱动力学研究对于理解复杂的神经网络行为至关重要.
研究的目的:
- 构建和控制一个中央循环记忆神经网络 (CCMNN).
- 调查CCMNN的复杂动态行为,包括混乱和共存的吸引力.
- 展示CCMNN在适应同步控制中用于安全通信的应用.
主要方法:
- 将memristors作为突触引入一个四维的霍普菲尔德神经网络.
- 数值分析包括平衡点稳定性,相轨迹图,分叉图和莱普诺夫指数 (LEs).
- 设计一个适应同步控制器用于有限时间控制,并通过硬件电路和NIST测试进行验证.
主要成果:
- CCMNN表现出复杂的动态,如混乱,分叉和共存的吸引力 (同质和异质).
- 与参数变化观察到的不对称的异质吸引子共存 (周期-周期,周期-稳定点,周期-混乱,稳定点-混乱).
- 通过调整结构参数,在不改变混乱状态的情况下实现振幅控制.
结论:
- CCMNN有效地模拟复杂的神经动态,并表现出多稳定的行为.
- 适应同步控制是可行的,显示安全通信的潜力.
- 硬件验证证实了模型的准确性和理论分析.
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