MMC_仿真模型

目录：1.体系结构 31.1体系结构 31.１.１B/S体系结构 31.2系统运行环境 31.2.1　网络结构图 31.2.2 硬件环境 31.2.3软件环境 32.界面设计 42.1后台界面设计 42.2前台界面计 63.数据模型 63.1数据库的概念结构模型设计 63.2数据库的逻辑结构模型设计 63.3数据库管理物理结构模型设计 84.模块设计 94.1病人登录端模块设计 124.1.1表示层设计 124.1.2控制层 144.2管理员登录模块设计 144.2.1表示层设计 144.2.2控制层 154.3专家查询模块设计 164.3.1表示

稀疏自编码深度学习的Matlab实现，sparse Auto coding，Matlab codetrain, m/7% CS294A/CS294W Programming Assignment Starter CodeInstructions%%%This file contains code that helps you get started ontheprogramming assignment. You will need to complete thecode in sampleIMAgEsml sparseAutoencoder Cost m and computeNumericalGradientml For the purpose of completing the assignment, you domot need tochange the code in this filecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencodtrain.m∥%%========%6% STEP 0: Here we provide the relevant parameters valuesthat willl allow your sparse autoencoder to get good filters; youdo not need to9 change the parameters belowvisibleSize =8*8; number of input unitshiddensize 25number of hidden unitssparsity Param =0.01; desired average activation ofthe hidden units7 (This was denoted by the greek alpharho, which looks like a lower-case pcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod4/57train.,m∥in the lecture notes)1 ambda=0.0001%o weight decay parameterbeta 3%o weight of sparsity penalty term%%==:79 STEP 1: Implement sampleIMAGESAfter implementing sampleIMAGES, the display_networkcommand shouldfo display a random sample of 200 patches from the datasetpatches sampleIMAgES;display_network(patches(:, randi(size(patches, 2), 204, 1)), 8)%为产生一个204维的列向量,每一维的值为0~10000curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod5/57train.m/v%中的随机数,说明是随机取204个 patch来显示%o Obtain random parameters thetatheta= initializeParameters ( hiddenSize, visibleSize)%%=============三三三三====================================97 STEP 2: Implement sparseAutoencoder CostYou can implement all of the components (squared errorcost, weight decay termsparsity penalty) in the cost function at once, butit may be easier to do%o it step-by-step and run gradient checking (see STEP3 after each stepWecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod6/57train. m vb suggest implementing the sparseAutoencoder Cost functionusing the following steps(a) Implement forward propagation in your neural networland implement the%squared error term of the cost function. Implementbackpropagation tocompute the derivatives. Then (using lambda=beta=(run gradient Checking%to verify that the calculations corresponding tothe squared error costterm are correctcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod7/57train. m vl(b) Add in the weight decay term (in both the cost funcand the derivativecalculations), then re-run Gradient Checking toverify correctnessl (c) Add in the sparsity penalty term, then re-run gradiChecking toverify correctnessFeel free to change the training settings when debuggingyour%o code. (For example, reducing the training set sizecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod8/57train m vl/number of hidden units may make your code run fasterand setting betaand/or lambda to zero may be helpful for debuggingHowever, in yourfinal submission of the visualized weights, please useparameters web gave in Step 0 abovecoS七grad]sparseAutoencoderCost(theta, visibleSize,hiddensize, lambda,sparsityParam, beta,patches)二〓二二二二二二二〓二〓二〓二〓=二====〓=curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod9/57train.m vlll96% STeP 3: Gradient CheckingHint: If you are debugging your code, performing gradienchecking on smaller modelsand smaller training sets (e. g, using only 10 trainingexamples and 1-2 hiddenunits) may speed things upl First, lets make sure your numerical gradient computationis correct for a%o simple function. After you have implemented computeNumerun the followingcheckNumericalGradientocurer:YiBinYUyuyibintony@163.com,WuYiUniversityDeep Learning, MATLAB Code for Sparse Autoencode10/57

内含三个文件，都是模型的三维点云，有需要的可以下载

完整版的CPLEX教程，共八章。CPLEX入门者必备资料

直接扩频仿真，基于matlab的误码率仿真，包括流程图，代码和仿真结果。

《最优化方法与程序设计》一书作者为倪勤，本书系统地介绍了非线性优化基本理论、方法与程序设计。主要内容有：线搜索与信赖域法，最速下降法与牛顿法，共轭梯度法，拟牛顿法，非线性最小二乘问题的解法，罚函数法，可行方向法，二次规划问题的解法，序列二次规划法等。本书的主要阅读对象是数学专业的本科生与研究生，非数学专业的研究生，对优化方法感兴趣的教师与科学技术人员。读者需要具备微积分、线性代数和Matlab语言方面的初步知识。

【实例简介】检测霍尔传感器的值可以判断出转子的位置，再使能相应的上下桥臂，则能驱动电机运动；若要让电机持续转动，则必须再次检测传感器值及使能相应的上下桥臂。这里采用的是将霍尔传感器输出的三根线相边的IO口配置成外部中断，并且为边沿触发，在中断函数中加入传感器检测与上下桥臂切换程序，如此电机就能持续运转

用于solr搜索 mmseg4j分词 搜狗词库 words.dic------------------jarsogou-dic-utf8datawords.dic

软件开发35案例 很详细的开发流程。。。内部WORD文档。。。 很适合爱好软件开发者学习使用！

用粒子群算法来求解函数的最优值，标准粒子群算法。