Introduction of stage panel material from rackinthecases

本文介绍了一种环保耐用的工业成品平台材料,具有高技术含量,安全防火防滑;优雅多功能的Tuffcoat成品平台,满足高需求,耐用防水;经济实用的地毯成品平台,适用于学校、军队、政府活动及婚礼等场合。
 


Company Website: www.rackincases.com

Company B2C  www.rackinthecases.com, 

Personal Website: http://sales1.rackinthecases.com

Or you can contact Lily at bgt1@vip.163.com


Introduction of stage panel material from rackinthecases--http://sales1.rackinthecases.com

Industrial Finished Platform: Material of environmental friendly and durable! Adopted with high technology materials of safe, fireproof, anti-skip features, the industrial finished stage panel posses the excellences of flexibility, conglutination, perfect water resistance, abrasion resistance and insulation. This kind of material is applicable either for indoor or out door activities. It can be a best choice on almost all the occasions large or small.


 

Tuffcoat Finished Platform:

Material of elegant looking and versatile functions! Using new developed paint and traditional, delicate painting way, tuffcoat finished platform have all the merits to meet the highly demands. Durability, water resistance, adhesion character offer the performers most desirable stage.


 

Carpet Finished Platform:

Material of economy and practicality! Carpet finished platform, the most favorable material for stage platforms. New qualified carpet allows your platform dirty-free, easy to clean. Mainly use indoors and available in grey, black. It's popular for various activities in schools, armies, governments and for weddings, ceremony and enterprise events etc.

Company Website: www.rackincases.com

Company B2C  www.rackinthecases.com, 

Personal Website: http://sales1.rackinthecases.com

Or you can contact Lily at bgt1@vip.163.com

转载于:https://www.cnblogs.com/happylilyhappy/archive/2009/08/04/1538870.html

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,用于纳米定位系统的预测控制研究”展开,提出了一种结合Koopman算子理论与递归神经网络(RNN)的数据驱动建模方法,旨在对非线性纳米定位系统进行有效线性化建模,并实现高精度的模型预测控制(MPC)。该方法利用Koopman算子将非线性系统映射到高维线性空间,通过递归神经网络学习系统的动态演化规律,构建可解释性强、计算效率高的线性化模型,进而提升预测控制在复杂不确定性环境下的鲁棒性与跟踪精度。文中给出了完整的Matlab代码实现,涵盖数据预处理、网络训练、模型验证与MPC控制器设计等环节,具有较强的基于数据驱动的 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)可复现性和工程应用价值。; 适合人群:具备一定控制理论基础和Matlab编程能力的研究生、科研人员及自动化、精密仪器、机器人等方向的工程技术人员。; 使用场景及目标:①解决高精度纳米定位系统中非线性动态响应带来的控制难题;②实现复杂机电系统的数据驱动建模与预测控制一体化设计;③为非线性系统控制提供一种可替代传统机理建模的有效工具。; 阅读建议:建议结合提供的Matlab代码逐模块分析实现流程,重点关注Koopman观测矩阵构造、RNN网络结构设计与MPC控制器耦合机制,同时可通过替换实际系统数据进行迁移验证,深化对数据驱动控制方法的理解与应用能力。
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