Diary of 2008-01-28

今日雪势加大,早晨出门骑行困难重重,不仅亲眼目睹了路人摔倒与鸡蛋摔破的情景,自己也差点滑倒。虽然路上状况频发,但仍坚持按时到岗,却发现办公室里只有两位同事。午间前往购物中心未有所获,晚上则选择观看了一部获奖电影《苏州河》,但对其主题感到难以理解。
I thought that it stopped snowing, but it's harder than yesterday! I started to worry about my cycling plan, I got up at 8:00 and left, on the door, a man by bicycle was fall, a half of eggs in basket was broken.the broken eggs spilt all over the ground. It's very pity:(, and the same time, another one went to work was fall!. so I had to be careful in moving my foot.

Come in the office, only two colleagues were keeping on their seat. but it's 9:30 now. it's nothing to be surprised about that. A newer thought that we are start work at 10:00. the air conditioner is bad, COLD!

Afternoon, went to the Metro Centre, but I still have nothing and begin to go over the "Spring Reference", At 3:00, the staff in the Centre told us that they had a activity and must closed the door of the machine room.

When I arrived home, I was fall one after the other, Sigh!

in the evening, watch the old film named "Su Zhou River", It's said that it winned centain award. but I didn't glom onto what it's saying,
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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