CnOpenData 英国专利事务表

时间区间

1985-2023.12.31


字段展示

英国专利事务表
专利ID
序号
事件类型
日期
代码
描述

样本数据

专利ID序号事件类型日期代码描述
GB0000005D00Applications terminated before publication under section 16(1)2001/2/28AT
GB0000006D00Patent expired after termination of 20 years2020/1/29PE20Expiry date: 20200104
GB0000008D00Application withdrawn, taken to be withdrawn or refused ** after publication under section 16(1)2004/10/6WAP
GB0000011D00Applications terminated before publication under section 16(1)2001/1/31AT
GB0000012D00Applications terminated before publication under section 16(1)2001/4/11AT
GB0000013D00Application withdrawn, taken to be withdrawn or refused ** after publication under section 16(1)2001/4/25WAP
GB0000017D00Application withdrawn, taken to be withdrawn or refused ** after publication under section 16(1)2004/12/15WAP
GB0000018D00Patent ceased through non-payment of renewal fee2006/9/27PCNPEffective date: 20060105
GB0000019D00Applications terminated before publication under section 16(1)2001/1/31AT
GB0000020D00Applications terminated before publication under section 16(1)2000/5/3AT
GB0000027D00Applications terminated before publication under section 16(1)2001/4/18AT
GB0000028D00Applications terminated before publication under section 16(1)2001/4/18AT
GB0000029D00Applications terminated before publication under section 16(1)2001/4/11AT
GB0000030D00Applications terminated before publication under section 16(1)2001/4/18AT
GB0000034D00Applications terminated before publication under section 16(1)2001/4/11AT
GB0000036D00Applications terminated before publication under section 16(1)2001/4/11AT
GB0000037D00Application withdrawn, taken to be withdrawn or refused ** after publication under section 16(1)2004/10/6WAP
GB0000041D00Applications terminated before publication under section 16(1)2001/4/11AT
GB0000046D00Application withdrawn, taken to be withdrawn or refused ** after publication under section 16(1)2004/6/23WAP
GB0000049D00Applications terminated before publication under section 16(1)2001/3/28AT
GB0000054D00Patent ceased through non-payment of renewal fee2007/9/26PCNPEffective date: 20070105
GB0000055D00Applications terminated before publication under section 16(1)2001/3/28AT
GB0000057D00Application withdrawn, taken to be withdrawn or refused ** after publication under section 16(1)2004/10/6WAP
GB0000058D00Applications terminated before publication under section 16(1)2001/4/11AT
GB0000059D00Applications terminated before publication under section 16(1)2001/4/11AT
GB0000060D00Applications terminated before publication under section 16(1)2001/1/10AT
GB0000063D00Register noted 'licences of right' (sect. 46/1977)2008/4/30746Effective date: 20080408
GB0000063D01Patent ceased through non-payment of renewal fee2012/9/26PCNPEffective date: 20120105
GB0000064D00Applications terminated before publication under section 16(1)2001/4/11AT
GB0000066D00Applications terminated before publication under section 16(1)2001/4/4AT
GB0000068D00Applications terminated before publication under section 16(1)2001/4/4AT
GB0000071D00Applications terminated before publication under section 16(1)2001/4/11AT
GB0000072D00Applications terminated before publication under section 16(1)2001/4/11AT
GB0000073D00Applications terminated before publication under section 16(1)2001/4/11AT
GB0000075D00Applications terminated before publication under section 16(1)2001/4/11AT
GB0000077D00Patent ceased through non-payment of renewal fee2019/9/25PCNPEffective date: 20190105
GB0000080D00Patent ceased through non-payment of renewal fee2009/9/23PCNPEffective date: 20090105
GB0000081D00Applications terminated before publication under section 16(1)2001/5/2AT
GB0000084D00Patent ceased through non-payment of renewal fee2013/9/25PCNPEffective date: 20130105
GB0000085D00Applications terminated before publication under section 16(1)2000/8/30AT

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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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