基于labview的音乐播放器

家用防盗报警器的Verilog源代码及原理图

此教程用简单，平易近人的语言讲解了topsis 模糊综合评价模型，是初学者的一个好选择

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反向传播算法是人工神经网络训练时采用的一种通用方法，在现代深度学习中得到了大 规模的应用。全连接神经网络(多层感知器模型，MLP)，卷积神经网络(CNN)，循环神 经网络(RNN)中都有它的实现版本。算法从多元复合函数求导的链式法则导出，递推的 计算神经网络每一层参数的梯度值。算法名称中的“误差”是指损失函数对神经网络每一层 临时输出值的梯度。反向传播算法从神经网络的输出层开始，利用递推公式根据后一层的误 差计算本层的误差，通过误差计算本层参数的梯度值，然后将差项传播到前一层(w, x,)+b这个神经元接受的输入信号为向量(),向量()为输入向量的组合权重,为徧置项,是标量。神经儿对输入冋量进行加权求和,并加上偏置项最后经过激活函数变换产生输出为表述简洁,我们把公式写成向量和矩阵形式。对每个神经元,它接受的来自前一层神经元的输入为向量,本节点的权重向量为,偏置项为,该神经元的输出值为先计算输入向量与权重向量的内积,加上偏置项,再送入一个函数进行变换,得到输出这个函数称为激活函数,典型的是函数。为什么需要激活函数以及什么样的函数可以充当激活函数,在之前的公众号文章“理解神经网终的激活函数”中已经进行了介绍。神绎网络一般有多个层。第一层为输入层,对应输入向量,神绎元的数量等于特征向量的维数,这个层不对数据进行处理,只是将输入向量送入下一层中进行计算。中间为隐含层,可能有多个。最后是输出层,神经元的数量等于要分类的类别数,输出层的输岀值被用来做分类预测。下面我们来看一个简单神经网络的例了,如下图所示这个网络有层。第一层是输入层,对应的输入向量为,有个神经元,写成分量形式为(),它不对数据做任何处理,直接原样送入下一层。中间层有个神经元,接受的输入数据为向量,输出向量为,写成分量形式为。第三个层为输出层,接受的输入数据为向量,输出向量为,写成分量形式为()。第一层到第层的权重矩阵为(,第二层到第三层的权重矩阵为()。权重矩阵的每一行为一个权重向量,是层所有神经元到本层某一个神经儿的连接权重,这里的上标表小层数如果激活函数选用函数,则第二层神经元的输出值为+(-(+0)+(1+(0)(-(()第三层神经元的输出值为如果把代入上面二式中,可以将输出向量表示成输出向量的函数。通过调整权重矩阵和偏置项可以实现不同的函数映射,因此神经网终就是一个复合函数需要解决的·个核心问题是·旦神经网络的结构(即神经元层数,每层神经元数量)桷定之后,怎样得到权重矩阵和偏置项。这些参数是通过训练得到的,这是本文推导的核心任务个简单的例子首先以前面的层神经网络为例,推导损失函数对神经网络所有参数梯度的计算方法假设训练样本集中有个样本()。其中为输入向量,为标签向量。现在要确定神经网络的映射函数:什么样的函数能很好的解释这批训练栟本?答案是神经网络的预测输出要尽可能的接近样本的标签值,即在训练集上最小化预测误差,如果使用均方误差,则优化的目标为:∑‖()-其中()和都是向量,求和项内部是向量的范数平方,即各个分量的平方和。上面的误差也称为欧氏距离损失函数,除此之外还可以使用其他损失函数,如交叉熵、对比损失等。优化目标函数的自变量是各层的权重矩阵和梯度向量,一般情况下无法保证目标函数是凸函数,因此这不是一个凸优化问题,有陷入局部极小值和鞍点的风险(对于这些概念和问题之前的公众号文章“理解梯度下降法”,“理解凸优化”中己经做了详细介绍)这是神经网络之前一直被诟病的一个问题。可以使用梯度下降法进行求解,使用梯度下降法需要计算出损失函数对所有权重矩阵、偏置向量的梯度值,接下来的关键是这些梯度值的计算。在这里我们先将问题简化,只考虑对单个样本的损失函数()-‖后面如果不加说明,都使用这种单样木的损失函数。如果计算出了对单个样木损失函数的棁度值,对这些梯度值计算均值即可得到整个目标函数的梯度值。和(要被代入到网络的后一层中,是复合函数的内层变量,我们先考虑外层的和。权重矩阵是一个x的矩阵,它的两个行分别为向量(和是个维的列向量,它的两个元素为()和()。网络的输入是向量,第一层映射之后的输出是向量首先计算损失函数对权重矩阵每个元素的偏导数,将欧氏距离损尖函数展开,有((+))(())6(如果,即对权重矩阵第行的元素求导,上式分了中的后半部分对来说是常数。根据链式法则有S()+()O如果,即对矩阵第二行的元素求导,类似的有:可以统一写成可以发现,第一个下标决定了权重矩阵的第行和偏置向量的第个分量,第二个下标决定了向量的第个分量。这可以看成是一个列向量与一个行向量相乘的结果,写成矩阵形式为上式中乘法⊙为向量对应元素相乘,第二个乘法是矩阵乘法。是个维列向量,+也是一个维列向量,两个向量执行⊙运算的结果还是个维列向量。是一个元素的列向量,其转置为维行向量,前面这个:维列向量与的乘积为的矩阵,这正好与矩阵的尺寸相等。在上面的公式中,权重的偏导数在求和项中由部分组成,分别是网络输出值与真实标签值的误差激活区数的导数+(),本层的输入值。神经网络的输出值、激活函数的导数值本层的输入值都可以在正向传播吋得到,因此可以晑效的计算出来。对所有训练样本的偏导数计算均值,可以得到总的偏导数对偏置项的偏导数为:如果上式分子中的后半部分对来说是常数,有:()⊥()如果类似的有这可以统写成:写成矩阵形式为偏置项的导数由两部分组成,分别是神经网络预测值与真实值之间的误差,激活函数的导数值,与权重矩阵的偏导数相比唯一的区别是少了。接下来计算对和的偏导数,由于是复合函数的内层,情况更为复杂。()是个的短阵,它的个行向量为(),(,(,(。偏置项()是维向量,个分量分别是(),(,(),(。首先计算损失函数对的元素的偏导数:而上式分子中的两部分都有,因此都与有关。为了表述简活,我们令:根据链式法则有:其巾((和和都是标量和()是两个()向量的内积,的每一个分量都是()的函数。接下来计算和这里的一是个向量,衣示的每个分量分别对求导。当时有:后面个分量相对于求导变量(都是常数。类似的当时有:()0)(()和时的结果以此类推。综合起来有:同理有:()十如果令合并得到()()[()-)。()。()写成矩阵形式为()最后计算偏置项的偏导数()类似的我们得到:合并后得到()写成矩阵形式为:(0)至此,我得到了这个简单网络对所有参数的偏导数,接下来我们将这种做法推广到更般的情况。从上面的结果可以看岀一个规律,输出层的权重矩阵和偏置向量梯度计算公式中共用了()-)()对」隐含层也有类似的结果完整的算法现在考虑一般的情况。假设有个训练样本(),其中为输入向量,为标签向量。训练的目标是最小化样木标签值与神经网络预测值之闩的误差,如果使用均方误差,则优化的目标为:其中为神经网络所有参数的集合,包括各层的权重和偏置。这个最优化问题是·个不带约束条件的问题,可以用梯度下降法求解。上面的误差函数定义在整个训练样本集上,梯度下降法每一次迭代利用了所有训练样本,称为批量棁度卜降法。如果样木数量很大,每次迭代都用所有样木进计算成木太高。为了解决这个问题,可以采用单样本梯度下降法,我们将上面的损失函数写成对单个样本的损失函数之和:定义对单个样本()的损失函数为)=-()如果采用单个样本进行迭代,梯度下降法第次迭代时参数的更新公式为:nV如果要用所有样本进行迭代,根据单个样本的损失函数梯度计算总损失梯度即可,即所有样本梯度的均值用梯度下降法求解需要初始化优化变量的值。一般初始化为一个随机数,如用正态分布(a)产生这些随机数,其中G是一个很小的正数到日前为止还有一个关键问题没有解决:日标函数是一个多层的复合函数,因为神经网络中每一层都有权重矩阵和偏置向量,且每一层的输出将会作为下一层的输入。因此,直接计算损失函数对所有权重和偏置的梚度很复杂,需要使用复合函数的求导公式进行递推计算几个重要的结论在进行推导之前,我们首先来看下面几种复合函数的求导。又如下线性映射函数:其中是维向量,是×的矩阵,是维向量。问题:假设有函数,如果把看成常数,看成的函数,如何根据函数对的梯度值Ⅴ计算函数对的梯度值Ⅴ?根据链式法则,由于只和有关,和其他的≠无关,因此有:c∑(对于的所有元素有:写成矩阵形式为:问题:如果将看成常数,将看成的函数,如何根据V计算Ⅴ?由于任意的和所有的都有关系,根据链式法则有写成矩阵形式为这是一个对称的结果,在计算函数映射时用矩阵乘以向量得到,在求梯度时用矩阵的转置乘以的梯度得到的梯度。问题:如果有向量到向量的映射:

全新RTCM3.3协议完整版RTCM STANDARD 10403.3DIFFERENTIAL GNSS(GLOBAL NAVIGATION SATELLITE SYSTEMS)SERVICES – VERSION 3DEVELOPED BYRTCM SPECIAL COMMITTEE NO. 104OCTOBER 7, 2016COPYRIGHT©2016 RTCMRadio Technical Commission for Maritime Services1611 N. Kent St., Suite 605Arlington, Virginia 22209-214RTCM Paper 141-2016-SC104-STD000ocRTCMco00c30RTCM 10403. 3, Differential GNSS Global Navigation Satellite Systems)Services- Version 3, October 7, 2016This standard (referred to as version 3 has been developed by rtCm special Committee 104 as a moreefficient alternative to the standards entitled rtcm recommended standards for diffe rentialRecommended Standards for Differential gNss Global Navigation Satellite Systems Service, Version 2.x(Current version is 2. 3, now designated as RTCM 10402. 3. Service providers and vendors represented onthe SC104 Committee wanted a new standard that would be more efficient, easy to use, and more easilyadaptable to new situations. The main complaint was that the version 2. x parity scheme, which useswords with 24 bits of data followed by 6 bits of parity, was wasteful of bandwidth. Another complaint wasthat the parity was not independent from word to word. Still another was that even with so many bitsdevoted to parity the actual integrity of the message was not as high as it should be. Plus, 30-bit wordsare awkward to handle. the new standard version 3 is intended to correct these weaknessesUnlike Version 2. x, this standard does not include tentative messages The messages in Version 3 haveundergone testing for validity and interoperability and are considered to be permanent. amendments tothe standard may change the meaning of reserved bits or provide additional clarifying text, but no changeswill be made in the data fields. Changes will require new messages to be developed. In addition to themessages described in the current standard the committee continues to develop new messages whichare described in separately published amendments and periodically gathered into a new edition of thestandard. RTCM 10403x for dgNSS services is proving useful in supporting highly accurate differentialand kinematic positioning as well as a wide range of navigation applications worldwideNote that Version 3 messages are not compatible with Version 2. x. Since many receivers have beendesigned and programmed for use with Version 2. x messages, rtCm is maintaining both standards0402 3 and 10403, 3 as" standardsVersion 3.0The initial edition consisted primarily of messages designed to support real-time kinematic (RTK)operations. The reason for this emphasis is that rtk operation involves broadcasting a lot of informationand thus benefits the most from an efficient data format. Version 3.0 provided messages that supportGPS and gloNaSs rTK operations including code and carrier phase observables antenna parametersand ancillary system parametersVersion 3. 1(RTCM Standard 10403.1:The next edition, Version 3. 1 (RTCM Standard 10403. 1), incorporated GPS Network Corrections, whichenable a mobile receiver to obtain accurate rtk information valid over a large area. In addition, new GPSand GLoNaSS messages provide orbital parameters to assist in rapid acquisition a Unicode text messageis also provided for the transmission of textual data. Finally a set of messages are reserved for vendorswho want to encapsulate proprietary data in their broadcasts the gps Network Corrections enable amobile receiver to obtain accurate rtk information valid over a large area. the network rtk correctioninformation provided to a rover can be considered as interpolated corrections between the referencestations in the rtk network this interpolation is not perfect and varies with the actual conditions of theatmosphere. A residual interpolation error has to be expected. With sufficient redundancy in the RtKnetwork, the network server process can provide an estimate for residual interpolation errors. Suchquality estimates may be used by the rover to optimize the performance of rtk solutions The values maybe considered by the rover as a priori estimates only with sufficient tracking data available the rovermight be able to judge residual geometric and ionospheric errors itselfVersion 3. 1. Amendment 1:Amendments 1 was an extensive addition that adds rtcm messages containing transformation data andinformation about Coordinate reference Systems. For rtCm data supporting a rtk service, coordinatesare measured within the itrf or a regional realization surve yors and other users of rtk services mustnormally present their results in the coordinates of local datums. Therefore, coordinate transformationsare necessary. by having RTCM messages that contain transformation data and information about theCoordinate reference systems the users of the rtk service can obtain their results in the desired datumwithout any manual operations. the rtk service providers can then ensure that current information forthe computation of the transformations is always used. the convenience of this method will promote theacceptance of rtK servicesVersion 3. 1. amendment 2:Amendment 2 added residual error messages to support the use of Non-Physical or Computed referenceStations in a network rtk environmentVersion 3. 1. amendment 3:Amendment 3 addressed differences in the way gnss receiver manufacturers have implemented carrierphase encoding of some Version 3 messages so that carrier phase observations are in phase for all carrierphases of a specific frequency i e. they correct for quarter cycle phase shifts. others retain the quartercycle offset between the carrier phase observations in the data. this amendment documents the waydifferent manufacturers have handled the phase shift issue and prescribes a uniform approach for futureproducts.∨ersiⅰon3.1, Amendment4:Amendment 4 added sections 3.5.13 on glONASS Network rtK Correction Messages and 3.5. 14 on FKPNetwork Rtk Correction Messages Related revisions were also made elsewhere in the document.Version 3. 1. amendment 5Amendment 5 added section 3. 5. 12 on State Space Representation related revisions are also madeelsewhere in the document, along with some editorial correctionsVersion 3. 2(RTCM Standard 10403.2)Version 3.2 consolidates Version 3. 1 and all five amendments into a new edition, and it adds MultipleSignal Messages (MSM)as well. the Multiple Signal Message (MSm)format generates receiverobservables in the same way for all included satellite systems. the messages include compact and fullmessages for Pseudorange, PhaseRange, Carrier to Noise Ratio (standard and high resolution), andPhaseRangeratea table near the beginning of the standard lists which messages were included in each separate editionand amendment, so it should not be necessary for users to refer to older versions. Multiple signalMessages are a generic format that will be followed for all GNSs systems. version 3 originally consisted ofmessages for GPS and GLONASS, each in their own format Now with the imminent addition of signals forBeiDou, Galileo, and QZSS, as well as new signals provided by modernized GPS and GloNASS satellitesthe need for a consistent generic format became evident. service providers and users are urged to migrateto the MsM messages to make it easier to accommodate new gNss services(See The RTCM Multiple Signal Messages: A New Step in GNSS Data Standardization")Another newmessage is the gloNaSS Bias Information message. This message provides information which is intendedto compensate for the first-order inter-frequency phase range biases introduced by the reference receivercode- phase biasVersion 3.2, Amendment 1:Added Galileo F/NAv Satellite Ephemeris Data(msg. 1045 )and Bds MSM(msgs. 1121-1127)Version 3.2 amendment 2Added qzss ephemeris(msg. 1044 )and QZss MSm (msgs. 1111-1117Version 3. 3(RTCM Standard 10403.3)This new edition adds Satellite-Based Augmentation System Multiple Signal Messages to previouslydopted messages for GPS, GLONASS, Galileo, and QzssA new ephemeris message has been added for BeiDou(BDS)and a new I/NAV ephemeris message hasbeen added for Galileo. The new edition also reserves 100 messages be used exclusively by sc104 fornew message developmentFinally, the new edition makes consolidates previous amendments and makes numerous editorialImprovementsNavstar GPS Service, Version 2. x. Service providers and vendors represented on the scco000c30z1O2co00c30Contentsco00c30

考研电路大串讲习题详解，详细解题过程。

java编写的简单的教务系统，数据库采用mysql。主要功能是学生选课、教师成绩录入、管理员管理老师和学生等。

C#抽奖系统源代码绝对能用这是一款功能实用、界面美观的公司通用抽奖系统的源码，功能比较完整，主要实现了随机抽奖并且可以查看抽奖结果、抽奖设置、以及清空抽奖记录的功能。

采用C语言编写，运用数值迭代方法求矩阵的特征值和特征向量，包括高斯迭代法和杜利特尔迭代法，精度10^-12

提取图片的不变矩特征，然后利用该特征进行图片的识别，达到我们的分类。