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yckz
说明： 20 世纪70 年代以来,人们从工业过程的特点出发,寻找对模型精度要求不高而同样能实现高质量控制性能的方法,预测控制就是在这种背景下发展起来的。预测控制技术最初由Richalet 和Cutler 提出[1 ] ,它最大程度地结合了工业实际的要求,综合效果好,已经在理论和应用方面取得了显著进展,各种预测控制算法不断地产生并得到发展。预测控制算法具有三大本质特征:预测模型、滚动优化和反馈校正[2 ] 。它是不断滚动的局部优化,而非全局最优。预测控制的特点:建模方便 采用非最小化描述的离散卷积和模型,信息冗余量大,有利于提高系统的鲁棒性 采用滚动优化策略,使模型失配、畸变、干扰等引起的不确定性及时得到弥补,从而得到较好的动态控制性能 可推广到有约束条件、大迟延、非最小相位以及非线性等过程,对模型精度要求不高,跟踪性能良好,更适应于复杂工业过程控制。
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- 2020-12-04 10:59:25下载
- 积分：1
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thinking.in.flexible.engineering
thinking.in.flexible.engineering 大道至简 向你讲述 软件开发中 的两个内容：做什么和为什么做(thinking.in.flexible.engineering Road to SR telling you about the software development of two elements : what to do and why to do)
- 2007-04-22 10:36:09下载
- 积分：1
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MATLABprogram
非常适合MATLAB的初级使用者,其中包括数学运算,基本函数的编写功能等.(Very suitable for the primary users of MATLAB, including mathematics, the basic function of the preparation of functions.)
- 2008-07-15 13:48:48下载
- 积分：1
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OPNETsimulate-satellite
opnet仿真卫星通信指导pdf，较为详细，可供参考(opnet simulation of satellite communication guide pdf, more detailed, for reference)
- 2021-03-10 19:49:26下载
- 积分：1
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deep-learning-turior
一本英文版的深度学习教程，内容全面，值得得初学者学习。(A deep learning in English tutorial, comprehensive, is worth to beginners learning. )
- 2015-03-06 11:54:39下载
- 积分：1
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dSPACE
dSPACE培训教程入门，控制系统半实物仿真的最好工具学习资料(dSPACE training module entry, the control system of the best semi-physical simulation tool for learning materials)
- 2007-10-22 12:28:38下载
- 积分：1
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UPC1270H
UPC1270H.PDF
功能强大的功放驱动IC,可容易组装DIY功放.(UPC1270H.PDF)
- 2010-08-26 12:09:59下载
- 积分：1
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eetop.cn_FPGA数字信号处理实现原理及方法
本书介绍基于FPGA实现数字信号处理的原理与方法，作为Xilinx公司相关课程的培训教材(The FPGA implementation of DSP principle & method.)
- 2020-06-17 23:20:01下载
- 积分：1
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AMCAT-test-question-papers
说明： notes on optical fibre communication
- 2020-06-25 19:00:01下载
- 积分：1
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kaggle_diabetic-master
说明： A commented bash script to generate our final 2nd place solution can be found in make_kaggle_solution.sh.
Running all the commands sequentially will probably take 7 - 10 days on recent consumer grade hardware. If you have multiple GPUs you can speed things up by doing training and feature extraction for the two networks in parallel. However, due to the computationally heavy data augmentation it may be far less than twice as fast especially when working with 512x512 pixel input images.
You can also obtain a quadratic weighted kappa score of 0.839 on the private leaderboard by just training the 4x4 kernel networks and by performing only 20 feature extraction iterations with the weights that gave you the best MSE validation scores during training. The entire ensemble only achieves a slightly higher score of 0.845.
- 2019-05-11 15:31:21下载
- 积分：1
