Convolutional Neural Network System,Convolutional Neural Network Graduation Thesis

The data mining of neural network in the paper how to write

I send you a paper with4G时代的到来,Telecommunications market increasingly competitive,Customer resources become the focus of telecom enterprise competition.

The customer consumption behavior rule is an important part of customer knowledge,So customer segmentation based on consumer behavior cognition as the culmination of a telecom enterprise customer relationship management (CRM).

Using data mining algorithm for a specific set of customer consumption data analysis,Dig up interesting information,And according to these interesting conclusion further adjust the enterprise marketing strategy.

This article in view of the current telecommunication enterprises in4GCustomer segmentation deficiency of,Combined with the characteristics of the telecom enterprise customers through correlation analysis to realize the subdivision of telecom enterprises existing customers,Help telecom enterprises to realize reasonable classification of telecom customer,The marketing strategy of telecom enterprises put forward the opinions.

Through to an operator4GCustomer database for analysis,采用AprioriAlgorithm found customer interesting association rules between consumer behavior and consumer features,And according to these information for further analysis,For the perspective of marketing decision makers to provide a new thinking.

The research idea of this paper is on the sample data preprocessing after,The sample data is divided into in4G卡、换4G套餐、换4GTerminal 3 big customer group,Calculate the average monthly respectively againarpu值、月均mou值、月均dou值,最后利用ClementineSoftware for the three customer groups based on the three valuesMDLPThe principle of entropy group,To get the segmentation features customers.

And then to these customer base to do further study,利用AprioriAlgorithm to generate frequent itemsets,On the basis of frequent itemsets produce simple association rules,Dig up customer's consumer behavior and brand segmentation variables、arpu值、mou值和douValue the relationship between,Sums up the corresponding law,To help telecom enterprises find specific consumer groups consumption habits,以此为基础,The identified by the consumer groups targeted marketing.

The principle of convolution neural network for face recognition based on depth is what？

Is essentially a pattern recognition,The reality of abstract into a digital computer can understand常见的神经网络结构.If a picture is256色的,So the image of each pixel point,都是0到255In the middle of a value,So that you can convert an image to a matrix.How to identify the model of matrix？

With a relatively small matrix in this large matrix from left to right,From top to bottom sweep again,Every little matrix blocks within,You can count0到255每种颜色出现的次数,To express the characteristics of a block.

So by this time“扫描”,You got another matrix composed of many small matrix block feature.This a matrix matrix is smaller than the original？那就对了！

Then the matrix of this a little bit small,Again the steps above,A characteristic“浓缩”,Speaking with another meaning,Is it abstract.Finally after many times of abstract,You will be the original matrix into a1D by1维的矩阵,这就是一个数字.

And different pictures,Such as a cat,Or a dog,A bear,They have finally come to this number will be different.

So you put a cat,一个狗,A bear is abstracted to a digital,比如0.34,0.75,0.23,The purpose of this is to let the computer to directly identify the.

人脸,表情,年龄,These principles are similar,Just the initial samples will be large number,Finally by matrix concrete image abstraction into digital,因为计算机只认识数字.But the abstract function,会有所不同,Achieve the effect will be different.

卷积神经网络 有哪些改进的地方

卷积神经网络的研究的最新进展引发了人们完善立体匹配重建热情.从概念看,基于学习算法能够捕获全局的语义信息,比如基于高光和反射的先验条件,便于得到更加稳健的匹配.

目前已经探求一些两视图立体匹配,用神经网络替换手工设计的相似性度量或正则化方法.这些方法展现出更好的结果,并且逐步超过立体匹配领域的传统方法.

事实上,立体匹配任务完全适合使用CNN,因为图像对是已经过修正过的,因此立体匹配问题转化为水平方向上逐像素的视差估计.

与双目立体匹配不同的是,MVS的输入是任意数目的视图,这是深度学习方法需要解决的一个棘手的问题.

而且只有很少的工作意识到该问题,比如SurfaceNet事先重建彩色体素立方体,将所有像素的颜色信息和相机参数构成一个3D代价体,所构成的3D代价体即为网络的输入.

然而受限于3D代价体巨大的内存消耗,SurfaceNet网络的规模很难增大：SurfaceNet运用了一个启发式的“分而治之”的策略,对于大规模重建场景则需要花费很长的时间.

Why have figure convolution neural network？

本质上说,All the data in the world are topology,Is the network structure,If you can take the real network data collection、融合起来,这确实是实现了AIThe first step in the smart.

所以,How to use the deep learning to deal with these complex topology data,How to start a new figure data processing and intelligent algorithm of knowledge map isAI的一个重要方向.

Deep learning success in many fields mainly due to the rapid development of computing resources（如GPU）、A large amount of training data collection,There are deep learning from the Euclidean data（如图像、文本和视频）The effectiveness of the extract potential characterization of.

但是,尽管深度学习已经在欧几里得数据中取得了很大的成功,But the data generated from the non Euclidean domain has been more widely used,They need effective analysis.

Such as in the field of electronic commerce,A graph based learning system can use the interaction between users and products in order to realize high precision recommend.在化学领域,分子被建模为图,New drug research and development need to measure its biological activity.

在论文引用网络中,Paper by referencing relationship between connected to each other,Need to be divided into different categories.自2012年以来,Deep learning in the field of computer vision, and natural language processing two has been a huge success.

Suppose you have a picture,要做分类,Traditional methods need to manually extract some characteristic,比如纹理,颜色,Or some more advanced features.Then put these characteristics in the classifier, such as the random forest,Give to an output tag,Tell which category it is.

And deep learning is the input picture,经过神经网络,Direct output a label.Feature extraction and classification of one pace reachs the designated position,To avoid the manual to extract the features or artificial rules,Automation to extract features from the original data,Is a kind of end-to-end（end-to-end）的学习.

相较于传统的方法,Deep learning can learn to more efficient characteristics and the mode.The complexity of the figure data of existing machine learning algorithm proposed major challenge,Because the figure data is irregular.

Each figure size、节点无序,A picture of every node has a different number of adjacent nodes,Make some easy to calculate in the image of important operation（如卷积）Cannot be directly applied to figure.此外,现有机器学习算法的核心假设是实例彼此独立.

然而,Each instance of figure data associated with the other instance of around,含有一些复杂的连接信息,Used to capture data dependencies between,包括引用、A friend relationship and interaction between.最近,越来越多的研究开始将深度学习方法应用到图数据领域.

By deep learning progress in the field of drive,The researchers use for reference in the design of the neural network architecture of convolution network、Circulation network and depth from the ideas of the encoder.In order to deal with the complexity of the figure data,Generalization and definition of important operation in the past few years the rapid development.

Artificial neural network of paper

神经网络的是我的毕业论文的一部分4．Artificial neural network is logical thinking and intuitive is the basic way of two different.

逻辑性的思维是指根据逻辑规则进行推理的过程;它先将信息化成概念,并用符号表示,然后,根据符号运算按串行模式进行逻辑推理.这一过程可以写成串行的指令,让计算机执行.

然而,直观性的思维是将分布式存储的信息综合起来,结果是忽然间产生想法或解决问题的办法.

这种思维方式的根本之点在于以下两点:1.信息是通过神经元上的兴奋模式分布在网络上;2.信息处理是通过神经元之间同时相互作用的动态过程来完成的.人工神经网络就是模拟人思维的第二种方式.

这是一个非线性动力学系统,其特色在于信息的分布式存储和并行协同处理.虽然单个神经元的结构极其简单,功能有限,但大量神经元构成的网络系统所能实现的行为却是极其丰富多彩的.

4.1Artificial neural network to learn first to the principle of artificial neural network to a certain rule of learning to learn,然后才能工作.

现以人工神经网络对手写“A”、“B”两个字母的识别为例进行说明,规定当“A”输入网络时,应该输出“1”,而当输入为“B”时,输出为“0”.

所以网络学习的准则应该是：如果网络做出错误的判决,则通过网络的学习,应使得网络减少下次犯同样错误的可能性.

首先,给网络的各连接权值赋予(0,1)区间内的随机值,将“A”所对应的图像模式输入给网络,网络将输入模式加权求和、与门限比较、再进行非线性运算,得到网络的输出.

在此情况下,网络输出为“1”和“0”的概率各为50%,也就是说是完全随机的.这时如果输出为“1”(结果正确),则使连接权值增大,以便使网络再次遇到“A”模式输入时,仍然能做出正确的判断.

如果输出为“0”(即结果错误),则把网络连接权值朝着减小综合输入加权值的方向调整,其目的在于使网络下次再遇到“A”模式输入时,减小犯同样错误的可能性.

如此操作调整,当给网络轮番输入若干个手写字母“A”、“B”后,经过网络按以上学习方法进行若干次学习后,网络判断的正确率将大大提高.

这说明网络对这两个模式的学习已经获得了成功,它已将这两个模式分布地记忆在网络的各个连接权值上.当网络再次遇到其中任何一个模式时,能够做出迅速、准确的判断和识别.

一般说来,网络中所含的神经元个数越多,则它能记忆、识别的模式也就越多.

4.2The advantages and disadvantages of artificial neural network of artificial neural network due to the organization of neurons in the brain was simulated with the basic characteristics of the human brain function,为人工智能的研究开辟了新的途径,神经网络具有的优点在于:（1）The parallel distributed processing arrangement because the artificial neural network of neurons and not messy,往往是分层或以一种有规律的序列排列,信号可以同时到达一批神经元的输入端,这种结构非常适合并行计算.

同时如果将每一个神经元看作是一个小的处理单元,则整个系统可以是一个分布式计算系统,这样就避免了以往的“匹配冲突”,“组合爆炸”和“无穷递归”等题,推理速度快.

（2）Learnability of a relatively small artificial neural network can store a lot of expert knowledge,并且能根据学习算法,或者利用样本指导系统来模拟现实环境(称为有教师学习),或者对输入进行自适应学习(称为无教师学习),不断地自动学习,完善知识的存储.

（3）Robustness and fault tolerance with a large number of neurons and their mutual connection,具有联想记忆与联想映射能力,可以增强专家系统的容错能力,人工神经网络中少量的神经元发生失效或错误,不会对系统整体功能带来严重的影响.

而且克服了传统专家系统中存在的“知识窄台阶”问题.（4）Generalization ability of artificial neural network is a kind of nonlinear large-scale systems,这就提供了系统自组织和协同的潜力.它能充分逼近复杂的非线形关系.

当输入发生较小变化,其输出能够与原输入产生的输出保持相当小的差距.

（5）具有统一的内部知识表示形式,任何知识规则都可以通过对范例的学习存储于同一个神经网络的各连接权值中,便于知识库的组织管理,通用性强.

虽然人工神经网络有很多优点,但基于其固有的内在机理,人工神经网络也不可避免的存在自己的弱点:（1）最严重的问题是没能力来解释自己的推理过程和推理依据.

（2）神经网络不能向用户提出必要的询问,而且当数据不充分的时候,神经网络就无法进行工作.（3）神经网络把一切问题的特征都变为数字,把一切推理都变为数值计算,其结果势必是丢失信息.

（4）神经网络的理论和学习算法还有待于进一步完善和提高.4.3The development trend of neural network and the feasibility of neural network in diesel engine fault diagnosis for the modern condition monitoring and fault diagnosis of complex large system provides a new theoretical methods and technical implementation means.

神经网络专家系统是一类新的知识表达体系,与传统专家系统的高层逻辑模型不同,它是一种低层数值模型,信息处理是通过大量的简单处理元件(结点)之间的相互作用而进行的.

由于它的分布式信息保持方式,为专家系统知识的获取与表达以及推理提供了全新的方式.

它将逻辑推理与数值运算相结合,利用神经网络的学习功能、联想记忆功能、分布式并行信息处理功能,解决诊断系统中的不确定性知识表示、获取和并行推理等问题.

通过对经验样本的学习,将专家知识以权值和阈值的形式存储在网络中,并且利用网络的信息保持性来完成不精确诊断推理,较好地模拟了专家凭经验、直觉而不是复杂的计算的推理过程.

但是,该技术是一个多学科知识交叉应用的领域,是一个不十分成熟的学科.一方面,装备的故障相当复杂;另一方面,人工神经网络本身尚有诸多不足之处:（1）受限于脑科学的已有研究成果.

由于生理实验的困难性,目前对于人脑思维与记忆机制的认识还很肤浅.（2）尚未建立起完整成熟的理论体系.

目前已提出了众多的人工神经网络模型,归纳起来,这些模型一般都是一个由结点及其互连构成的有向拓扑网,结点间互连强度所构成的矩阵,可通过某种学习策略建立起来.但仅这一共性,不足以构成一个完整的体系.

这些学习策略大多是各行其是而无法统一于一个完整的框架之中.（3）带有浓厚的策略色彩.这是在没有统一的基础理论支持下,为解决某些应用,而诱发出的自然结果.（4）与传统计算技术的接口不成熟.

人工神经网络技术决不能全面替代传统计算技术,而只能在某些方面与之互补,从而需要进一步解决与传统计算技术的接口问题,才能获得自身的发展.

虽然人工神经网络目前存在诸多不足,但是神经网络和传统专家系统相结合的智能故障诊断技术仍将是以后研究与应用的热点.它最大限度地发挥两者的优势.

神经网络擅长数值计算,适合进行浅层次的经验推理;专家系统的特点是符号推理,适合进行深层次的逻辑推理.

智能系统以并行工作方式运行,既扩大了状态监测和故障诊断的范围,又可满足状态监测和故障诊断的实时性要求.既强调符号推理,又注重数值计算,因此能适应当前故障诊断系统的基本特征和发展趋势.

随着人工神经网络的不断发展与完善,它将在智能故障诊断中得到广泛的应用.根据神经网络上述的各类优缺点,目前有将神经网络与传统的专家系统结合起来的研究倾向,建造所谓的神经网络专家系统.

理论分析与使用实践表明,神经网络专家系统较好地结合了两者的优点而得到更广泛的研究和应用.离心式制冷压缩机的构造和工作原理与离心式鼓风机极为相似.

但它的工作原理与活塞式压缩机有根本的区别,它不是利用汽缸容积减小的方式来提高汽体的压力,而是依靠动能的变化来提高汽体压力.

离心式压缩机具有带叶片的工作轮,当工作轮转动时,叶片就带动汽体运动或者使汽体得到动能,然后使部分动能转化为压力能从而提高汽体的压力.

这种压缩机由于它工作时不断地将制冷剂蒸汽吸入,又不断地沿半径方向被甩出去,所以称这种型式的压缩机为离心式压缩机.其中根据压缩机中安装的工作轮数量的多少,分为单级式和多级式.

如果只有一个工作轮,就称为单级离心式压缩机,如果是由几个工作轮串联而组成,就称为多级离心式压缩机.在空调中,由于压力增高较少,所以一般都是采用单级,其它方面所用的离心式制冷压缩机大都是多级的.

单级离心式制冷压缩机的构造主要由工作轮、扩压器和蜗壳等所组成.

压缩机工作时制冷剂蒸汽由吸汽口轴向进入吸汽室,并在吸汽室的导流作用引导由蒸发器(或中间冷却器)来的制冷剂蒸汽均匀地进入高速旋转的工作轮3(工作轮也称叶轮,它是离心式制冷压缩机的重要部件,因为只有通过工作轮才能将能量传给汽体).

汽体在叶片作用下,一边跟着工作轮作高速旋转,一边由于受离心力的作用,在叶片槽道中作扩压流动,从而使汽体的压力和速度都得到提高.

由工作轮出来的汽体再进入截面积逐渐扩大的扩压器4(因为汽体从工作轮流出时具有较高的流速,扩压器便把动能部分地转化为压力能,从而提高汽体的压力).汽体流过扩压器时速度减小,而压力则进一步提高.

经扩压器后汽体汇集到蜗壳中,再经排气口引导至中间冷却器或冷凝器中.

二、The characteristics of the centrifugal compressor and the characteristics of centrifugal compressor compared with piston type refrigeration compressor,具有下列优点：(1)单机制冷量大,在制冷量相同时它的体积小,占地面积少,重量较活塞式轻5～8倍.

(2)由于它没有汽阀活塞环等易损部件,又没有曲柄连杆机构,因而工作可靠、运转平稳、噪音小、操作简单、维护费用低.(3)工作轮和机壳之间没有摩擦,无需润滑.

故制冷剂蒸汽与润滑油不接触,从而提高了蒸发器和冷凝器的传热性能.(4)能经济方便的调节制冷量且调节的范围较大.(5)对制冷剂的适应性差,一台结构一定的离心式制冷压缩机只能适应一种制冷剂.

(6)由于适宜采用分子量比较大的制冷剂,故只适用于大制冷量,一般都在25～30万大卡／时以上.如制冷量太少,则要求流量小,流道窄,从而使流动阻力大,效率低.

但近年来经过不断改进,用于空调的离心式制冷压缩机,单机制冷量可以小到10万大卡／时左右.制冷与冷凝温度、蒸发温度的关系.

由物理学可知,回转体的动量矩的变化等于外力矩,则T=m(C2UR2-C1UR1)两边都乘以角速度ω,得Tω=m(C2UωR2-C1UωR1)也就是说主轴上的外加功率N为：N=m(U2C2U-U1C1U)上式两边同除以m则得叶轮给予单位质量制冷剂蒸汽的功即叶轮的理论能量头.

U2C2ω2C2UR1R2ω1C1U1C2rβ离心式制冷压缩机的特性是指理论能量头与流量之间变化关系,也可以表示成制冷W=U2C2U-U1C1U≈U2C2U（因为进口C1U≈0）又C2U=U2-C2rctgβC2r=Vυ1/(A2υ2)故有W=U22(1-Vυ1ctgβ)A2υ2U2式中：V—叶轮吸入蒸汽的容积流量（m3/s）υ1υ2——分别为叶轮入口和出口处的蒸汽比容（m3/kg）A2、U2—叶轮外缘出口面积(m2)与圆周速度(m/s)β—On the blade installation Angle by type visible,理论能量头W与压缩机结构、转速、冷凝温度、蒸发温度及叶轮吸入蒸汽容积流量有关.

对于结构一定、转速一定的压缩机来说,U2、A2、β皆为常量,则理论能量头W仅与流量V、蒸发温度、冷凝温度有关.

按照离心式制冷压缩机的特性,宜采用分子量比较大的制冷剂,目前离心式制冷机所用的制冷剂有F—11、F—12、F—22、F—113和F—114等.

我国目前在空调用离心式压缩机中应用得最广泛的是F—11和F—12,且通常是在蒸发温度不太低和大制冷量的情况下,选用离心式制冷压缩机.

此外,在石油化学工业中离心式的制冷压缩机则采用丙烯、乙烯作为制冷剂,只有制冷量特别大的离心式压缩机才用氨作为制冷剂.

三、The adjustment of the centrifugal compressor centrifugal compressor and other refrigeration equipment constitute an energy supply and consumption system.

制冷机组在运行时,只有当通过压缩机的制冷剂的流量与通过设备的流量相等时,以及压缩机所产生的能量头与制冷设备的阻力相适应时,制冷系统的工况才能保持稳定.

但是制冷机的负荷总是随外界条件与用户对冷量的使用情况而变化的,因此为了适应用户对冷负荷变化的需要和安全经济运行,就需要根据外界的变化对制冷机组进行调节,离心式制冷机组制冷量的调节有：1°改变压缩机的转速;2°采用可转动的进口导叶;3°改变冷凝器的进水量;4°进汽节流等几种方式,其中最常用的是转动进口导叶调节和进汽节流两种调节方法.

所谓转动进口导叶调节,就是转动压缩机进口处的导流叶片以使进入到叶轮去的汽体产生旋绕,从而使工作轮加给汽体的动能发生变化来调节制冷量.

所谓进汽节流调节,就是在压缩机前的进汽管道上安装一个调节阀,如要改变压缩机的工况时,就调节阀门的大小,通过节流使压缩机进口的压力降低,从而实现调节制冷量.

离心式压缩机制冷量的调节最经济有效的方法就是改变进口导叶角度,以改变蒸汽进入叶轮的速度方向(C1U)和流量V.但流量V必须控制在稳定工作范围内,以免效率下降.

cnn Paper convolutional neural network at the university of Toronto

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A few surviving college as a North America federal university（Similar to the Oxford University）,In addition to the conventional architecture,At the university of Toronto, the subordinates have12The undergraduate college,Each have different history and characteristics of,Enjoy greater degree of independent financial and management,In the heart of the city's main campus outside,The university of Toronto and the university of Toronto, sega fort campus and two satellite campus at the university of Toronto, dense xisha campus.