This folder has some scritps that you may find usefull. All of it comes from questions that I ve received in my email. If you have a new request/question, feel free to send it to marceloperlin@gmail.com. But please, don t ask me to do your homework. Passing_your_param0 This folder contains instructions (and m files) for passing you own initial parameters to the fitting function. I also included a simple simulation script that will create random initial coefficients (under the proper bounds) and fit the model to the data.
標(biāo)簽： that questions scritps usefull
上傳時(shí)間： 2013-12-28
上傳用戶：netwolf
自己編的一個(gè)用隨機(jī)行走法實(shí)現(xiàn)電路計(jì)算的例子，電路圖及參數(shù)均在Partial random walk for large linear network analysis里
上傳時(shí)間： 2016-05-19
上傳用戶：teddysha
Numerical Computing with MATLAB （by Cleve Moler) is a textbook for an introductory course in numerical methods, Matlab, and technical computing. The emphasis is on in- formed use of mathematical software. We want you learn enough about the mathe- matical functions in Matlab that you will be able to use them correctly, appreciate their limitations, and modify them when necessary to suit your own needs. The topics include * introduction to Matlab, * linear equations, * interpolation, * zero and roots, * least squares, * quadrature, * ordinary di?erential equations, * random numbers, * Fourier analysis, * eigenvalues and singular values, * partial di?erential equations.
標(biāo)簽： introductory Numerical Computing textbook
上傳時(shí)間： 2016-07-04
上傳用戶：思琦琦
Toolbox for Numerical Computing with MATLAB （by Cleve Moler). Numerical Computing with MATLAB （by Cleve Moler) is a textbook for an introductory course in numerical methods, Matlab, and technical computing. The emphasis is on in- formed use of mathematical software. We want you learn enough about the mathe- matical functions in Matlab that you will be able to use them correctly, appreciate their limitations, and modify them when necessary to suit your own needs. The topics include * introduction to Matlab, * linear equations, * interpolation, * zero and roots, * least squares, * quadrature, * ordinary di?erential equations, * random numbers, * Fourier analysis, * eigenvalues and singular values, * partial differential equations.
標(biāo)簽： Numerical Computing MATLAB with
上傳時(shí)間： 2014-01-01
上傳用戶：guanliya
Digital Signal and Image Processing Using MATLAB The most important theoretical aspects of image and signal processing (ISP) for both deterministic and random signals are covered in this guide to using MATLAB® . The discussion is also supported by exercises and computer simulations relating to real applications such as speech processing and fetal-heart–rhythm tracking, and more than 200 programs and functions for numerical experiments are provided with commentary.
標(biāo)簽： theoretical Processing important Digital
上傳時(shí)間： 2016-07-08
上傳用戶：z1191176801
本人編寫的incremental 隨機(jī)神經(jīng)元網(wǎng)絡(luò)算法，該算法最大的特點(diǎn)是可以保證approximation特性，而且速度快效果不錯(cuò)，可以作為學(xué)術(shù)上的比較和分析。目前只適合benchmark的regression問(wèn)題。 具體效果可參考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006.
標(biāo)簽： incremental 編寫 神經(jīng)元網(wǎng)絡(luò) 算法
上傳時(shí)間： 2016-09-18
上傳用戶：litianchu
The idea behind differential GPS is to remove as much errors as possible from the range measurements by establishing these errors at a reference site. In its most simple setup, a GPS receiver is located at a well surveyed position and its (pseudo) range measurements are compared with the actual calculated range from this receiver to the SV s. The differences between measured ranges and calculated ranges at the reference receiver are applied as corrections to the ranges measured by other receiver(s) close by.
標(biāo)簽： differential measurements possible behind
上傳時(shí)間： 2013-12-19
上傳用戶：zgu489
PRINCIPLE: The UVE algorithm detects and eliminates from a PLS model (including from 1 to A components) those variables that do not carry any relevant information to model Y. The criterion used to trace the un-informative variables is the reliability of the regression coefficients: c_j=mean(b_j)/std(b_j), obtained by jackknifing. The cutoff level, below which c_j is considered to be too small, indicating that the variable j should be removed, is estimated using a matrix of random variables.The predictive power of PLS models built on the retained variables only is evaluated over all 1-a dimensions =(yielding RMSECVnew).
標(biāo)簽： from eliminates PRINCIPLE algorithm
上傳時(shí)間： 2016-11-27
上傳用戶：凌云御清風(fēng)
AMOP 0.3 is an Automatic Mock Object for C++. By using ABI and template techniques, it can simulate a pseudo-"Reflection" which is normally not support in C++ . The main differences between AMOP and other mock object library is that, users DO NOT need to implement the interface of the object which to mock...
標(biāo)簽： techniques Automatic template simulate
上傳時(shí)間： 2013-12-31
上傳用戶：rocwangdp
The VGA example generates a 320x240 diffusion-limited-aggregation (DLA) on Altera DE2 board. A DLA is a clump formed by sticky particles adhering to an existing structure. In this design, we start with one pixel at the center of the screen and allow a random walker to bounce around the screen until it hits the pixel at the center. It then sticks and a new walker is started randomly at one of the 4 corners of the screen. The random number generators for x and y steps are XOR feedback shift registers (see also Hamblen, Appendix A). The VGA driver, PLL, and reset controller from the DE2 CDROM are necessary to compile this example. Note that you must push KEY0 to start the state machine.
標(biāo)簽： diffusion-limited-aggregation DLA generates 320x240
上傳時(shí)間： 2014-01-16
上傳用戶：225588
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