在概率性比特翻转下马科维亚跳跃神经网络的状态估计:分配受约束的比特率
概括
本研究涉及马科维亚跳跃神经网络 (MJNNs) 在无线网络上的状态估计. 我们开发了一种新的传输机制和估计器,以确保可靠的状态估计,尽管带宽限制和位翻转.
科学领域:
- 控制系统工程 控制系统工程
- 网络化系统 网络化系统
- 人工智能的人工智能
背景情况:
- 马科维亚跳跃神经网络 (MJNNs) 的状态估计对于控制系统至关重要.
- 无线通信道带来了诸如限制带宽和位翻转等挑战,影响了估计准确性.
研究的目的:
- 调查在数字网络限制下对MJNN的状态估计.
- 制定一个强大的远程估计策略,考虑到带宽限制和传输错误.
主要方法:
- 无线传输的数学建模与比特率约束和概率比特翻转.
- 对MJNN的模式依赖远程估计器的设计.
- 对于有界估计误差的足够条件的推导.
主要成果:
- 一种新的传输机制准确地描述了在约束条件下的数字传输.
- 拟议的估计器有效地捕捉了MJNN的内部状态.
- 这项研究确定了神经网络估计性能和比特率之间的关系.
结论:
- 开发的理论框架确保了在具有挑战性的无线环境中对MJNN的可靠状态估计.
- 模拟结果验证了拟议方法和理论发现的有效性.
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