My Lost Certificates Come back Magically

Today aunt Wang, an administrator of our Young Teacher Apartment, made a phone call to me and asked me to take my certificates from her. Hearing her out, I went downstairs quickly to her room, full of surprise in my mind, to wonder which certificate was left in administrator’s room.

Catching sight of my work certificate and identity card lost two months ago, I was taken aback. It is unimaginable that after two months, my certificates appear in front of me magically. From aunt Wang, I knew that my certificates were found by students in a covert corner in the dormitory of YanZhao No.1 Apartment, which was next to ours, when they cleaned their room. Now I was acquainted with the truth of the matter to some extent. It is self-evident that my missing handbag was really stolen by thieves, maybe when I washed my face in the morning. Being useless for the thieves, certificates in my handbag were thrown to the dormitory of YanZhao No.1 Apartment from our apartment through the window.


     Unfortunately, I have handled new work certificate as well as new identity card already. My old identity card becomes invalid. However, both my new and old work certificate are available, for work certificate doesn’t need to be reported loss.


     Are thieves poor and hard up? Do they feel comfortable when spending money stolen? I will never know their feelings. Undeniably it is high time to better the public security, while there is still a long way to go towards solving the problem. For ourselves, the only thing we can do is heightening our vigilance and taking care of our own things.                    
基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的Koopman算子的递归神经网络模型线性化”展开,旨在研究纳米定位系统的预测控制方法。通过结合数据驱动技术与Koopman算子理论,将非线性系统动态近似为高维线性系统,进而利用递归神经网络(RNN)建模并实现系统行为的精确预测。文中详细阐述了模型构建流程、线性化策略及在预测控制中的集成应用,并提供了完整的Matlab代码实现,便于科研人员复现实验、优化算法并拓展至其他精密控制系统。该方法有效提升了纳米级定位系统的控制精度与动态响应性能。; 适合人群:具备自动控制、机器学习或信号处理背景,熟悉Matlab编程,从事精密仪器控制、智能制造或先进控制算法研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①实现非线性动态系统的数据驱动线性化建模;②提升纳米定位平台的轨迹跟踪与预测控制性能;③为高精度控制系统提供可复现的Koopman-RNN融合解决方案; 阅读建议:建议结合Matlab代码逐段理解算法实现细节,重点关注Koopman观测矩阵构造、RNN训练流程与模型预测控制器(MPC)的集成方式,鼓励在实际硬件平台上验证并调整参数以适应具体应用场景。
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