本代碼包為本人的一篇文章<一個(gè)占用內(nèi)存極少的菜單系統(tǒng)的實(shí)現(xiàn)>在在PC上的測(cè)試移植代碼。 ------------------------------ Menu_Src目錄為Menu的源代碼 Ks0108.C的void Display_Locate(unsigned char DisplayData, unsigned char X, unsigned char Y)函數(shù)為最底層的顯示函數(shù)。 該函數(shù)調(diào)用LCD模擬函數(shù)來(lái)完成顯示。 KeyScan.C的unsigned char KeyScan(void)函數(shù)為鍵盤模擬函數(shù)。 void DelayMs( WORD time ) 延時(shí) ------------------------------ GUI_SIM.exe為編譯后的文件,可以直觀看到這個(gè)GUI的效果. PC鍵盤的4個(gè)按鍵控制菜單周轉(zhuǎn): PC按鍵 菜單中功能 up 向上鍵 確定鍵 進(jìn)入子菜單 down向下鍵 取消鍵 返回父菜單 left向左鍵 向上鍵 菜單項(xiàng)上一項(xiàng) right向右鍵 向下鍵 菜單項(xiàng)下一項(xiàng) 有興趣自己編譯VC工程:\Project\Menu.dsw <一個(gè)占用內(nèi)存極少的菜單系統(tǒng)的實(shí)現(xiàn)>相關(guān)PDF文檔和其他資料在以下鏈接: http://www.ouravr.com/bbs/bbs_content.jsp?bbs_sn=798580&bbs_page_no=3&bbs_id=9999
上傳時(shí)間： 2014-06-24
上傳用戶：stvnash
計(jì)算二日期的間隔天數(shù)，計(jì)算某日期為星期幾，打印對(duì)象當(dāng)前數(shù)據(jù)的y年m月的月歷，一次增加若干天，對(duì)兩個(gè)日期進(jìn)行其他比較運(yùn)算等。
標(biāo)簽： 計(jì)算
上傳時(shí)間： 2016-12-18
上傳用戶：
視頻編碼電路主要實(shí)現(xiàn)接收8位CCIR656格式的YUV數(shù)據(jù)，（例如MPEG解碼數(shù)據(jù)），并編碼成亮度Y和色度信號(hào)C，以及合成CVBS信號(hào)，經(jīng)過(guò)D/A轉(zhuǎn)換后輸出。基本的編碼功能包括副載波產(chǎn)生，色差信號(hào)調(diào)制，同步信號(hào)內(nèi)插。 主要應(yīng)用在視頻處理，軍事圖像處理。 GM7221設(shè)計(jì)原理圖
上傳時(shí)間： 2013-12-29
上傳用戶：Divine
//下面是畫(huà)圓的程序， //畫(huà)線、畫(huà)圓、畫(huà)各種曲線其實(shí)都很簡(jiǎn)單，歸根到底就是x、y的二元方程嘛 //對(duì)算法感興趣的話建議去找本《計(jì)算機(jī)圖形學(xué)》看看，不是賣關(guān)子哦。實(shí)在是幾句話說(shuō)不清除，呵呵 // ---------------------------------------------- //字節(jié) void circleDot(unsigned char x,unsigned char y,char xx,char yy)//內(nèi)部函數(shù)，對(duì)稱法畫(huà)圓的8個(gè)鏡像點(diǎn) {//對(duì)稱法畫(huà)圓的8個(gè)鏡像點(diǎn)
標(biāo)簽： 程序
上傳時(shí)間： 2014-01-07
上傳用戶：秦莞爾w
Batch version of the back-propagation algorithm. ％ Given a set of corresponding input-output pairs and an initial network ％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the ％ network with backpropagation. ％ ％ The activation functions must be either linear or tanh. The network ％ architecture is defined by the matrix NetDef consisting of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer. ％
標(biāo)簽： back-propagation corresponding input-output algorithm
上傳時(shí)間： 2016-12-27
上傳用戶：exxxds
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(shí)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(shí)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(shí)間： 2016-12-27
上傳用戶：腳趾頭
【歐拉算法】 微分方程的本質(zhì)特征是方程中含有導(dǎo)數(shù)項(xiàng),數(shù)值解法的第一步就是...歐拉(Euler)算法是數(shù)值求解中最基本、最簡(jiǎn)單的方法,但其求解精度較低,一般不在...對(duì)于常微分方程: dy/dx=f(x,y),x∈[a,b] y(a)=y0 可以將區(qū)
上傳時(shí)間： 2014-01-09
上傳用戶：www240697738
#include "iostream.h" #include "iomanip.h" #define N 20 //學(xué)習(xí)樣本個(gè)數(shù) #define IN 1 //輸入層神經(jīng)元數(shù)目 #define HN 8 //隱層神經(jīng)元數(shù)目 #define ON 1 //輸出層神經(jīng)元數(shù)目 double P[IN] //單個(gè)樣本輸入數(shù)據(jù) double T[ON] //單個(gè)樣本教師數(shù)據(jù) double W[HN][IN] //輸入層至隱層權(quán)值 double V[ON][HN] //隱層至輸出層權(quán)值 double X[HN] //隱層的輸入 double Y[ON] //輸出層的輸入 double H[HN] //隱層的輸出
標(biāo)簽： define include iostream iomanip
上傳時(shí)間： 2014-01-01
上傳用戶：凌云御清風(fēng)
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