Flex chip implementation File: UP2FLEX JTAG jumper settings: down, down, up, up Input: Reset - FLEX_PB1 Input n - FLEX_SW switches 1 to 8 Output: Countdown - two 7-segment LEDs. Done light - decimal point on Digit1. Operation: Setup the binary input n number. Press the Reset switch. See the countdown from n down to 0 on the 7-segment LEDs. Done light lit when program terminates.
標(biāo)簽： down implementation settings UP2FLEX
上傳時間： 2014-01-21
上傳用戶：sclyutian
This function calculates Akaike s final prediction error ％ estimate of the average generalization error. ％ ％ [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the ％ final prediction error estimate (fpe), the effective number of ％ weights in the network if the network has been trained with ％ weight decay, an estimate of the noise variance, and the Gauss-Newton ％ Hessian. ％
標(biāo)簽： generalization calculates prediction function
上傳時間： 2014-12-03
上傳用戶：maizezhen
％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is possible to use regularization by ％ weight decay. Also pruned (ie. not fully connected) networks can ％ be trained. ％ ％ Given a set of corresponding input-output pairs and an initial ％ network, ％ [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms) ％ trains the network with the Levenberg-Marquardt method. ％ ％ The activation functions can be either linear or tanh. The ％ network architecture is defined by the matrix NetDef which ％ has two rows. The first row specifies the hidden layer and the ％ second row specifies the output layer.
標(biāo)簽： Levenberg-Marquardt desired network neural
上傳時間： 2016-12-27
上傳用戶：jcljkh
This function calculates Akaike s final prediction error ％ estimate of the average generalization error for network ％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive ％ counterparts. ％ ％ [FPE,deff,varest,H] = nnfpe(method,NetDef,W1,W2,U,Y,NN,trparms,skip,Chat) ％ produces the final prediction error estimate (fpe), the effective number ％ of weights in the network if it has been trained with weight decay, ％ an estimate of the noise variance, and the Gauss-Newton Hessian. ％
標(biāo)簽： generalization calculates prediction function
上傳時間： 2016-12-27
上傳用戶：腳趾頭
XML+ASP 強(qiáng)大的自動生成靜態(tài)產(chǎn)品目錄網(wǎng)頁實例,可完全代替數(shù)據(jù)庫+服務(wù)端程序的網(wǎng)站設(shè)計模式.優(yōu)點在于： １．它只需在自己的配置有ＩＩＳ或其它ＡＳＰ執(zhí)行軟件的電腦上執(zhí)行一次便自動生成大量（上傳的示例會生成兩千多頁）靜態(tài)html網(wǎng)頁，你只需將這些靜態(tài)網(wǎng)頁和圖片發(fā)布到網(wǎng)站服務(wù)器上去就行了，因此，在服務(wù)器不支持?jǐn)?shù)據(jù)庫或服務(wù)器端程序如ASP, JSP等的情況下照樣可用． ２．網(wǎng)上有許多工具能方便的修改xml，你完全不需要學(xué)習(xí)數(shù)據(jù)庫知識和安裝數(shù)據(jù)庫軟件． 上傳的代碼是已經(jīng)應(yīng)用的代碼． 提示： １．由于以前寫的時候沒有考慮給別人看的，所以沒寫太多注釋，請見諒，不過ＡＳＰ結(jié)構(gòu)是很清淅的 ２．javascript 裝在一個.js文件里且經(jīng)過了壓縮，目的是節(jié)省空間，這在生成大量網(wǎng)頁時很重要．如需修改請用專門的script修改工具． 效果可以看下： http://www.samluo.com/light/light.htm 這個目錄里的兩千多文件全是由一個ASP文件預(yù)先生成的．
標(biāo)簽： XML ASP 自動生成 產(chǎn)品目錄
上傳時間： 2014-01-22
上傳用戶：Avoid98
learning English The following appeared in a memorandum written by the vice president of Nature s Way, a chain of stores selling health food and other health-related products. "Previous experience has shown that our stores are most profitable in areas where residents are highly concerned with leading healthy lives. We should therefore build our next new store in Plainsville, which has many such residents. Plainsville merchants report that sales of running shoes and exercise clothing are at all-time highs. The local health club, which nearly closed five years ago due to lack of business, has more members than ever, and the weight training and aerobics classes are always full. We can even anticipate a new generation of customers: Plainsville s schoolchildren are required to participate in a fitness for life program, which emphasizes the benefits of regular exercise at an early age.
標(biāo)簽： memorandum following president learning
上傳時間： 2017-03-06
上傳用戶：youth25
C:\Documents and Settings\Administrator\桌面\VC++多媒體特效制作百例\CHAR12\Light
標(biāo)簽： SettingsAdministrator Documents Light CHAR
上傳時間： 2017-03-07
上傳用戶：lo25643
In this project we analyze and design the minimum mean-square error (MMSE) multiuser receiver for uniformly quantized synchronous code division multiple access (CDMA) signals in additive white Gaussian noise (AWGN) channels.This project is mainly based on the representation of uniform quantizer by gain plus additive noise model. Based on this model, we derive the weight vector and the output signal-to-interference ratio (SIR) of the MMSE receiver. The effects of quantization on the MMSE receiver performance is characterized in a single parameter named 鈥漞quivalent noise variance鈥? The optimal quantizer stepsize which maximizes the MMSE receiver output SNR is also determined.
標(biāo)簽： mean-square multiuser receiver project
上傳時間： 2014-11-21
上傳用戶：ywqaxiwang
awesome is a highly configurable, next generation framework window manager for X. It is very fast, light, and extensible. It is primarily targeted at the power user, developer, and anyone dealing with everyday computing tasks who wants to have fine-grained control over a graphical environment.
標(biāo)簽： X. configurable generation framework
上傳時間： 2013-12-26
上傳用戶：banyou
This thesis presents a comprehensive overview of the problem of facial recognition. A survey of available facial detection algorithms as well as implementation and tests of di鏗€erent feature extraction and dimensionality reduction methods and light normalization methods are presented.
標(biāo)簽： comprehensive recognition presents overview
上傳時間： 2017-05-05
上傳用戶：royzhangsz
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