The MINI2440 is an effecient ARM9 development board with a comprehensive price, it characterizes SIMple method and high performance-price ratio. Based on the Samsung S3C2440 microprocessor, it embodies professional stable CPU core power source chip and reset chip to ensure the stability of the system operation. The PCB on the MINI2440 board is designed to be 4-layers board, adopting the ENIG technology and professional equal-length wiring to ensure the completeness of the signals of the key signal wires and manufactured and released under stringent quality control plans. With the help of this detailed manual, users are supposed to become proficient in the development process of embedded Linux and WinCE operating system, they are supposed to get the foundation, so long as they have obtained the basic and necessary knowledge about the C language, in two weeks.
標(biāo)簽：
comprehensive
characterizes
development
effecient
上傳時(shí)間：
2013-12-18
上傳用戶：csgcd001
% 生成訓(xùn)練樣本集
clear all;
clc;
P=[110 0.807 240 0.2 15 1 18 2 1.5;
110 2.865 240 0.1 15 2 12 1 2;
110 2.59 240 0.1 12 4 24 1 1.5;
220 0.6 240 0.3 12 3 18 2 1;
220 3 240 0.3 25 3 21 1 1.5;
110 1.562 240 0.3 15 3 18 1 1.5;
110 0.547 240 0.3 15 1 9 2 1.5];
0 1.318 300 0.1 15 2 18 1 2];
T=[54248 162787 168380 314797;
28614 63958 69637 82898;
86002 402710 644415 328084;
230802 445102 362823 335913;
60257 127892 76753 73541;
34615 93532 80762 110049;
56783 172907 164548 144040];
@907 117437 120368 130179];
m=max(max(P));
n=max(max(T));
P=P'/m;
T=T'/n;
%-------------------------------------------------------------------------%
pr(1:9,1)=0; %輸入矢量的取值范圍矩陣
pr(1:9,2)=1;
bpnet=newff(pr,[12 4],{'logsig', 'logsig'}, 'traingdx', 'learngdm');
%建立BP神經(jīng)網(wǎng)絡(luò)， 12個(gè)隱層神經(jīng)元，4個(gè)輸出神經(jīng)元
%tranferFcn屬性 'logsig' 隱層采用Sigmoid傳輸函數(shù)
%tranferFcn屬性 'logsig' 輸出層采用Sigmoid傳輸函數(shù)
%trainFcn屬性 'traingdx' 自適應(yīng)調(diào)整學(xué)習(xí)速率附加動(dòng)量因子梯度下降反向傳播算法訓(xùn)練函數(shù)
%learn屬性 'learngdm' 附加動(dòng)量因子的梯度下降學(xué)習(xí)函數(shù)
net.trainParam.epochs=1000;%允許最大訓(xùn)練步數(shù)2000步
net.trainParam.goal=0.001; %訓(xùn)練目標(biāo)最小誤差0.001
net.trainParam.show=10; %每間隔100步顯示一次訓(xùn)練結(jié)果
net.trainParam.lr=0.05; %學(xué)習(xí)速率0.05
bpnet=train(bpnet,P,T);
%-------------------------------------------------------------------------
p=[110 1.318 300 0.1 15 2 18 1 2];
p=p'/m;
r=SIM(bpnet,p);
R=r'*n;
display(R);
標(biāo)簽：
2013
算法
工具箱
上傳時(shí)間：
2016-05-28
上傳用戶：shanqiu
