Talking to myself

本文强调了专注于目标的重要性,建议清空杂念、保持低调、善待他人,并在做决定前冷静思考。作者分享了自我激励的方法,认为写作就像歌唱与低语,能帮助平复心情。

You have your goal and your motivation; what else do you need?

Don't distract your focus.

Make clear thing clearer; don't think of other things.

Empty your mind. There is only one goal that you need to achieve first.

You want to earn respects from others, which I totally understand, but you have to know it's much harder to get respects from friends than from strangers.

The more you learn, the more aspects you will find you don't understand. 

Stay low key. Be nice.

Don't rush to make a decision. When you want to, deep breathing, breathing... Calm down, Calm down.

When I lose direction or courage, I will be here to analyse the problem and encourage myself.

Writing is like singing and whispering in order to wait for the boiling water becoming cool.

There will be me and another me. A guy with problems, another guy is mentor guiding to find root cause.

I like doing this. It works and is effective. Feel much better.

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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