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Description: you can study TCP_IP step by step!
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Size: 1830912 |
Author: wangye |
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Description: In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
-In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar-xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
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Size: 197632 |
Author: 晨间 |
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Description: 在一种变步长LMS 算法的基础上，引进动量因式，提出了一种新的改进LMS 的算法。新算法整体性能优
于变步长LMS 算法以及LMS 算法。通过理论分析，比较了新的算法和变步长LMS 算法以及LMS 算法的收敛性
和稳态性，提出了一种设想以提高新算法的稳态性。仿真试验证明了新算法的优越性以及设想的在仿真条件下的正
确性。-In a variable step size LMS algorithm, based on the introduction of momentum factor, a new improvement of LMS algorithms. The overall performance of the new algorithm is superior to variable step size LMS algorithm and LMS algorithm. Through theoretical analysis, comparing the new algorithm and variable step size LMS algorithm and LMS algorithm convergence and steady-state, and proposes a new algorithm is envisaged to enhance the steady-state nature. Simulation tests proved the superiority of the new algorithm, as well as envisaged under the conditions in the simulation is correct.
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Size: 139264 |
Author: 王勇 |
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Description: 用matlab自编的的变步长LMS滤波的算法的程序，希望对大家有用-Matlab own use of variable step size LMS algorithm for filtering procedure, in the hope that useful to everybody
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Size: 1024 |
Author: 王瑞玲 |
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Description: 一种变步长LMS算法及其Matlab仿真.pdf，应用科技，吕强-A Variable Step Size LMS Algorithm and Matlab simulation. Pdf, Applied Science and Technology, Lu Qiang
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Size: 199680 |
Author: cg |
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Description: 步进电机的控制程序，完全可以应用在实际的工业控制中-Stepper motor control program can be used in practical industrial control
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Size: 251904 |
Author: 张哓 |
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Description: protel原理图
H桥电机驱动器
特点：5-7V低电压供电，带升压电路产生12V以上栅极驱动电压，两片so-8小体积mos管半桥驱动芯片保证驱动效果
本电路已应用到多个直流电机驱动板上，最大驱动电流可根据所选用的mos管调整-Protel schematic H bridge motor driver Features :5-7V low-voltage power supply, with step-up circuit for more than 12V gate drive voltage, so-8 2 mos small volume tube-driven half-bridge driver IC to ensure that the effect of this circuit has been applied to a number of DC motor drive board, the biggest drive current according to the selected pipe adjust mos
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Size: 4096 |
Author: 刘大川 |
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Description: 病态系统求解方法比较：龙格库塔4固定步长、变步长、吉尔法-Pathological system solving methods: Runge-Kutta 4 fixed step size, variable step size, Gill Law
Platform: |
Size: 1024 |
Author: loser zeng |
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Description: 步进电机驱动,单步驱动,完整代码,用keil c51编写的.-Stepper motor-driven, single-step drive, complete code, with keil c51 prepared.
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Size: 17408 |
Author: lwc |
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Description: 新三步搜索算法对三步算法进行了适当的改进，在一定程度上弥补了TSS算法在估计细小运动时的不足，并加入了半途中止的策略，进一步提高了搜索精度
-The new three-step search algorithm for three-step algorithm to improve the appropriate, to a certain extent make up for the TSS algorithm in estimating the inadequacy of a small movement, and joined the mid-suspension strategy to further improve the search accuracy
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Size: 2048 |
Author: 赵光 |
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Description: 电气电路控制 已利用到产品 供参考 并提出修改意见 硬件:SIEMENS S7-2000 CN 可编程序控制器 软件环境: STEP 7 MicroWIN V4.0 SP3-Electric circuit control products have been used for reference and to propose amendments to the hardware: SIEMENS S7-2000 CN PLC software environment: STEP 7 MicroWIN V4.0 SP3
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Size: 4096 |
Author: ffx |
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Description: 上海步科电气有限公司的canopen现场总线卡，对于自动化学习有帮助。-Step Electric Co., Ltd. Shanghai Branch of the CANopen fieldbus cards, for automation to help with learning.
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Size: 287744 |
Author: yang |
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Description: 这 里主要对LMS算法及一些改进的LMS算法（NLMS算法、变步长LMS算法、变换域LMS算法）之间的不同点进行了比较，在传统的LMS算法的基础上发 展了LMS算法的应用。另一方面又从RLS算法的分析中对其与LMS算法的不同特性进行了比较。-Here mainly on the LMS algorithm and some improvements of the LMS algorithm (NLMS algorithm, variable step size LMS algorithm, transform domain LMS algorithm) between the different points of comparison, in the traditional LMS algorithm developed on the basis of the application of the LMS algorithm. On the other hand from the analysis of RLS algorithm and LMS algorithm for its different characteristics compared.
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Size: 30720 |
Author: jj |
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Description: 一本C#的经典书籍，学习工作都用得上Microsoft.Visual.C.Sharp.2005.Step.by.Step.Oct.2005.chm-A C# Classic books, study and work are useful Microsoft.Visual.C.Sharp.2005.Step.by.Step.Oct.2005.chm
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Size: 1669120 |
Author: sunny |
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Description: 程序名：ga_bp_predict.cpp
描述： 采用GA优化的BP神经网络程序，用于单因素时间
序列的预测,采用了单步与多步相结合预测
说明： 采用GA(浮点编码)优化NN的初始权值W[j][i],V[k][j]，然后再采用BP算法
优化权值-Program name: ga_bp_predict.cpp Description: The GA-optimized BP neural network procedure for single-factor time series prediction using the single-step and multi-step prediction combining Description: using GA (floating point coding) to optimize the initial NN weights W [j] [i], V [k] [j], then BP algorithm to optimize the use of weights
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Size: 6144 |
Author: fk774 |
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Description: Step 7 梯形图编程，可供初学者参考。写的不是很好，谢谢支持！-Step 7 ladder programming reference for beginners. Writing is not very good, I would like to thank the support!
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Size: 186368 |
Author: 觊觎 |
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Description: 若不希望用与估计输入信号矢量有关的相关矩阵来加快LMS算法的收敛速度，那么可用变步长方法来缩短其自适应收敛过程，其中一个主要的方法是归一化LMS算法（NLMS算法），变步长 的更新公式可写成
W（n+1）=w（n）+ e（n）x（n）
=w（n）+ （3.1）
式中， = e（n）x（n）表示滤波权矢量迭代更新的调整量。为了达到快速收敛的目的，必须合适的选择变步长 的值，一个可能策略是尽可能多地减少瞬时平方误差，即用瞬时平方误差作为均方误差的MSE简单估计，这也是LMS算法的基本思想。
-Want to estimate if the input signal vector and the relevant matrix to speed up the convergence rate of LMS algorithm, then the variable step size method can be used to shorten its adaptive convergence process, one of the main method is normalized LMS algorithm (NLMS algorithm) , variable step-size update formula can be written W (n+ 1) = w (n)+ e (n) x (n) = w (n)+ (3.1) where, = e (n) x (n) the right to express filter update vector iterative adjust the volume. In order to achieve the purpose of fast convergence, we must choose the appropriate value of variable step size, a possible strategy is as much as possible to reduce the instantaneous squared error, which uses the instantaneous squared error as the mean square error MSE of the simple estimate, which is the basic LMS algorithm思想.
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Size: 3072 |
Author: 闫丰 |
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Description: 步进电机定位控制系统VHDL程序,可以进行步进角的倍数设定,激磁方式的选择-Stepper motor positioning control system for VHDL process can be carried out in multiples of step angle setting, the choice of excitation mode
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Size: 4096 |
Author: wavy |
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Description: struts2.0入门介绍，有详细的实例，按步骤绝对可以实现-Getting Started struts2.0 introduction, detailed examples, step-by-step can definitely realize
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Size: 9216 |
Author: simon |
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Description: 迄今为止见过的最方便的huffman编码，效率很高
一个外国人写的，很具有研究价值
Constructing a Huffman Tree according to the number of times each symbol appears in the data stream:
1) Create an array of N nodes, representing N possible symbols (ranging between 0 and N-1).
2) Set the value of each node to the number of times that its symbol appears in the data stream.
3) Create a Minimum-Heap of N nodes.
4) Add every node whose value is greater than zero to the heap.
5) Extract the best two nodes in the heap.
6) Create a parent node whose children are the two extracted nodes.
7) Add the parent node to the heap.
8) Repeat the previous three steps N-1 times (until only 1 node remains in the heap).
9) Extract the last node in the heap.
The array created in the first step stores the leaves of the tree, and is used in order to encode the data stream.
The node extracted in the last step is in fact the root of the tree, and is used in order to decode the data stream.-So far seen the most convenient huffman coding, efficient write a foreigner, I have research value Constructing a Huffman Tree according to the number of times each symbol appears in the data stream: 1) Create an array of N nodes , representing N possible symbols (ranging between 0 and N-1) .2) Set the value of each node to the number of times that its symbol appears in the data stream.3) Create a Minimum-Heap of N nodes.4) Add every node whose value is greater than zero to the heap.5) Extract the best two nodes in the heap.6) Create a parent node whose children are the two extracted nodes.7) Add the parent node to the heap.8) Repeat the previous three steps N-1 times (until only 1 node remains in the heap) .9) Extract the last node in the heap.The array created in the first step stores the leaves of the tree, and is used in order to encode the data stream.The node extracted in the last step is in fact the root of the tree, and is used in order to decode the data stream.
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Size: 17408 |
Author: 游弋人生 |
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