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问题,并提供完整的Matlab代码实现。文章结合数据驱动方法与Koopman算子理论,利用递归神经网络(RNN)对非线性系统进行建模与线性化处理,从而提升纳米级定位系统的精度与动态响应性能。该方法通过提取系统隐含动态特征,构建近似线性模型,便于后续模型预测控制(MPC)的设计与优化,适用于高精度自动化控制场景。文中还展示了相关实验验证与仿真结果,证明了该方法的有效性和先进性。; 适合人群:具备一定控制理论基础和Matlab编程能力,从事精密控制、智能制造、自动化或相关领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①应用于纳米级精密定位系统(如原子力显微镜、半导体制造设备)中的高性能控制设计;②为非线性系统建模与线性化提供一种结合深度学习与现代控制理论的新思路;③帮助读者掌握Koopman算子、RNN建模与模型预测控制的综合应用。; 阅读建议:建议读者结合提供的Matlab代码逐段理解算法实现流程,重点关注数据预处理、RNN结构设计、Koopman观测矩阵构建及MPC控制器集成等关键环节,并可通过更换实际系统数据进行迁移验证,深化对方法泛化能力的理解。
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