Behavioral model of pipeline ADC by using SIMULINK(R)
黑龙江省大庆市让胡路区大庆市大庆中学2023-2024学年高二下学期7月期末英语试题
黑龙江省大庆市让胡路区大庆市大庆中学2023-2024学年高二下学期7月期末英语试题一、听力选择题1．What are the speakers talking about?A．Breakfast.B．Lunch.C．Dinner.2．What may prevent the woman from buying the table?A．Its quality.B．Its price.C．Its design.3．What is the probable relationship between the speakers?A．Husband and wife.B．Co-workers.C．Doctor and patient. 4．What does the man need to do next?A．Turn into another road.B．Slow down his car.C．Avoid the road sign. 5．When did the woman arrive in the town?A．On July 9th.B．On July 10th.C．On July 11th.听下面一段较长对话，回答以下小题。
6．Where does the conversation probably take place?A．At a record store.B．At a bookstore.C．At a gift store.7．What will the man do next?A．Play some music.B．Write something down.C．Prepare for a birthday party.听下面一段较长对话，回答以下小题。
8．Who is the man?A．The woman’s husband.B．A regular customer.C．The woman’s neighbor. 9．Which dessert has a strong flavor?A．The lemon pie.B．The strawberry cake.C．The green tea cupcake. 10．How much should the man pay in total?A．$3.B．$4.C．$7.听下面一段较长对话，回答以下小题。
pipeline adc原理
pipeline adc原理
PipelineADC原理是一种高速、高精度的模数转换器设计方法。
它通过将模拟信号分段转换为数字信号，逐级进行逼近操作，最终输出所需的数字结果。
具体来说，Pipeline ADC由多个级联的子ADC
组成，每个子ADC都负责一部分的位数转换，通过将结果送到下一级ADC以便进行进一步的处理，最终得到所需的数字结果。
与传统的逐次逼近ADC相比，Pipeline ADC具有更高的采样速率和更高的有效位数，但也需要更多的电路复杂度和功耗。
在高速、高精度、大量数据转换等领域中，Pipeline ADC被广泛应用。
- 1 -。
稀疏距离扩展目标自适应检测及性能分析
第39卷第7期自动化学报Vol.39,No.7 2013年7月ACTA AUTOMATICA SINICA July,2013稀疏距离扩展目标自适应检测及性能分析魏广芬1苏峰2简涛2摘要在球不变随机向量杂波背景下,研究了稀疏距离扩展目标的自适应检测问题.基于有序检测理论,利用协方差矩阵估计方法,分析了自适应检测器(Adaptive detector,AD).其中,基于采样协方差矩阵(Sample covariance matrix,SCM)和归一化采样协方差矩阵(Normalized sample covariance matrix,NSCM),分别建立了AD-SCM和AD-NSCM检测器.从恒虚警率特性和检测性能综合来看,AD-NSCM的性能优于AD-SCM和已有的修正广义似然比检测器.最后,通过仿真实验验证了所提方法的有效性.关键词稀疏距离扩展目标,自适应检测,采样协方差矩阵,归一化采样协方差矩阵,有序统计量引用格式魏广芬,苏峰,简涛.稀疏距离扩展目标自适应检测及性能分析.自动化学报,2013,39(7):1126−1132DOI10.3724/SP.J.1004.2013.01126Sparsely Range-spread Target Detector and Performance AssessmentWEI Guang-Fen1SU Feng2JIAN Tao2Abstract In the background where the clutter is modeled as a spherically invariant random vector,the adaptive detection of sparsely range-spread targets is addressed.By exploiting the order statistics and the covariance matrix estimators,the adaptive detector(AD)is assessed.Herein,the detectors of AD-SCM and AD-NSCM are proposed based on the sample covariance matrix(SCM)and normalized sample covariance matrix(NSCM),respectively.In terms of constant false alarm rate properties and detection performance,the AD-NSCM outperforms the AD-SCM and the existing detector of modified generalized likelihood ratio.Finally,the performance assessment conducted by simulation confirms the effectiveness of the proposed detectors.Key words Sparsely range-spread target,adaptive detection(AD),sample covariance matrix(SCM),normalized sample covariance matrix(NSCM),order statisticsCitation Wei Guang-Fen,Su Feng,Jian Tao.Sparsely range-spread target detector and performance assessment.Acta Automatica Sinica,2013,39(7):1126−1132低分辨率雷达的目标尺寸小于距离分辨率,这种目标常称之为点目标[1].通过采用脉冲压缩技术,高分辨率雷达能够在空间上把一个目标分解成许多散射点[2−3],目标回波在雷达径向上的多个散射点分布在不同的距离分辨单元中,形成距离扩展目标[4].在许多情况下,距离扩展目标的散射点密度是稀疏的,可将这种目标简称为“稀疏距离扩展目标”.目前,高斯背景下的距离扩展目标检测已取得一定进收稿日期2011-12-28录用日期2012-08-27Manuscript received December28,2011;accepted August27, 2012国家自然科学基金(61174007,61102166),山东省优秀中青年科学家科研奖励基金(BS2010DX022)资助Supported by National Natural Science Foundation of China (61174007,61102166)and the Scientific Research Founda-tion for Outstanding Young Scientists of Shandong Province (BS2010DX022)本文责任编委韩崇昭Recommended by Associate Editor HAN Chong-Zhao1.山东工商学院信息与电子工程学院烟台2640052.海军航空工程学院信息融合技术研究所烟台2640011.School of Information and Electronics,Shandong Institute of Business and Technology,Yantai2640052.Research Insti-tute of Information Fusion,Naval Aeronautical and Astronauti-cal University,Yantai264001展,其中,针对估计参数空间过大的问题,文献[5]提出了一种无需辅助数据的检测器,简称为修正的广义似然比检验(Modified generalized likelihood ratio test,MGLRT)检测器,其在高斯背景下是有界恒虚警率(Constant false alarm rate,CFAR)的.但在高距离分辨率的条件下,背景杂波呈现出诸多的非高斯特性[1],高斯背景下获得的检测器已无法有效检测目标.在非高斯背景下,文献[6]研究了已知杂波协方差矩阵条件下的距离扩展目标检测;而通过利用不含目标信号的辅助数据,文献[7]和文献[8]分别针对距离扩展目标和距离–多普勒二维分布式目标展开了自适应检测研究.需要指出的是,以上自适应检测方法[7−8]都是基于辅助数据的.当无法获得满足条件的辅助数据时,实现非高斯背景下距离扩展目标的自适应检测具有重要意义.文献[9]基于迭代估计方法实现了自适应检测,但迭代估计计算量较大,如何在保证性能的同时减小计算量,也是值得探讨的问题.7期魏广芬等:稀疏距离扩展目标自适应检测及性能分析1127稀疏距离扩展目标的散射点只占据目标距离扩展范围的一部分,与含纯杂波的距离分辨单元幅值相比,含目标散射点的距离分辨单元幅值明显更高,这就为实现目标的自适应检测提供了条件.本文针对非高斯杂波中的稀疏距离扩展目标检测问题,在不需要辅助数据的条件下,首先,采用有序统计检测理论和协方差矩阵估计方法,粗略估计目标散射点单元集合;然后,进一步利用适当估计方法获得协方差矩阵的精确估计,设计了自适应检测器(Adaptivedetector,AD),并通过仿真实验验证了检测器的有效性.1问题观测数据来源于N个阵元的线性阵列天线,需跨过K个可能存在目标的距离分辨单元z t,t=1,···,K,判决一个距离扩展目标的存在与否.假设可能的目标完全包含在这些数据中,并且忽略目标距离走动的问题.在杂波背景下,待解决的检测问题可由以下二元假设检验公式来表达.H0:z t=c t,t=1,···,KH1:z t=αt p+c t,t=1,···,K(1)其中,p=(1,e jφ,e j2φ,···,e j(N−1)φ)T/√N表示已知单位导向矢量,即p H p=1,这里(·)H表示共轭转置,φ表示相移常量,(·)T表示转置,αt,t=1,···,K是反映目标幅度的未知参数.非高斯杂波可用球不变随机向量建模[10],由于中心极限定理在较小区域的杂波范围内仍是有效的,球不变随机向量可以表示为两个分量的乘积:一个是反映受照区域反射率的时空“慢变化”纹理分量,另一个是变化“较快”的“散斑”高斯过程.那么,距离分辨单元t的N维杂波向量c t为c t=√τt·ηt,t=1,···,K(2)其中,ηt=(ηt(1),ηt(2),···,ηt(N))T是零均值协方差矩阵为Σ的复高斯随机向量,非负的纹理分量τt与ηt相互独立,其用来描述杂波功率在不同距离分辨单元间的起伏,且服从未知分布fτ.另外,杂波协方差矩阵结构Σ可以表示为Σ=E{ηt ηHt}(3)距离扩展目标完全包含在K个距离分辨单元的滑窗中,假设一个等效散射点最多只占据一个距离分辨单元,即目标等效散射点数目与其所占据的距离分辨单元数目是相等的.通常目标散射点是稀疏分布的,与含纯杂波的距离分辨单元相比,有散射点的距离分辨单元幅值往往更高.含目标等效散射点的距离分辨单元数目用h0表示,而其所对应的距离分辨单元下标用集合Θh表示.为了简化分析,假设h0是已知的,若其未知,可利用模型阶数选择方法获得合适的估计值[11].如前所述,对距离扩展目标的检测只需在距离分辨单元Θh内进行,式(1)表示的假设检验问题可以进一步表示为H0:z t=c t,t∈ΘhH1:z t=αt p+c t,t∈Θh(4)在分布fτ未知的条件下,距离分辨单元t的杂波是条件高斯的,其相应的方差为τt.由于幅度αt 未知而向量p已知,针对不同假设,观测向量z t的联合概率密度可表示为t∈Θhf(z t|τt,H0)=t∈Θh1πNτN t det(Σ)×exp[−1τtz HtΣ−1z t](5)t∈Θhf(z t|αt,τt,H1)=t∈Θh1πNτN t det(Σ)×exp−1τt(z t−αt p)HΣ−1(z t−αt p)(6)其中,det(·)表示方阵的行列式.2检测器实现在未知集合Θh的条件下,为了获得估计的参数集合ˆΘh,这里先假设已知矩阵Σ.由于未知参数α={αt|t∈Θh}和τ={τt|t∈Θh},可利用广义似然比检验(GLRT)原理进行检测器设计[12].在矩阵Σ已知的条件下,根据GLRT原理,对于似然比中的未知参数,可用最大似然(Maximum likelihood,ML)估计进行替换,即考虑如下二元判决:maxτmaxαt∈Θhf(z t|αt,τt,H1)maxτt∈Θhf(z t|τt,H0)H1><H0T0(7)在H1假设下求得αt的ML估计为[13]ˆαt=p HΣ−1z tp HΣ−1p(8)将ˆαt代入式(7)后,可进一步在不同假设条件下求得τt的ML估计:H0:ˆτt=1Nz HtΣ−1z t(9) H1:ˆτt=1N(z t−ˆαt p)HΣ−1(z t−ˆαt p)(10)1128自动化学报39卷将式(8)∼(10)代入式(7)中,可得自然对数形式的GLRT判决为λ1=−Nt∈Θh0ln1−|p HΣ−1z t|2(z H tΣ−1z t)(p HΣ−1p)H1><H0T1(11)令w t=|p HΣ−1z t|2(z H tΣ−1z t)(p HΣ−1p)(12)值得注意的是,w t的结构类似于一个归一化匹配滤波器(权向量为Σ−1p)[14].可以看出,式(12)的分子部分p HΣ−1z t等效于给定距离分辨单元观测z t经过匹配滤波后的结果[14].而分母部分的两项z HtΣ−1z t和p HΣ−1p起到了归一化处理的作用,因此,w t是距离单元观测z t经过匹配滤波后模平方的归一化,可以看作是距离单元观测经归一化匹配滤波后的能量.由于目标完全包含在K个单元的距离滑窗中,且距离扩展目标等效散射点所占据的距离分辨单元幅值往往大于纯杂波的距离分辨单元幅值,因此,可通过归一化能量w t,t=1,···,K中最大的h0个值来确定未知集合ˆΘh.实际应用中协方差矩阵结构Σ往往是未知的,为了确定集合ˆΘh,需先对协方差矩阵结构进行估计.如前所述,纹理分量τt的分布fτ是未知的,因此,协方差矩阵结构Σ的ML估计不能通过期望最大化得到[13].本文考虑两种协方差矩阵估计方法.一种是高斯背景下的经典采样协方差矩阵(Sample covariance matrix,SCM),其可以表示为ˆΣSCM =1RRr=1y r y Hr(13)其中,y r,r=1,···,R表示可用于估计的R个数据.当R≥N时,SCM是以概率为1非奇异的,同时也是正定Hermitian矩阵[12].另外,在非高斯背景下,也常常利用辅助数据获得归一化采样协方差矩阵(Normalized sample covariance matrix, NSCM),可以表示为ˆΣNSCM =1RRr=1Ny Hry ry r y Hr(14)与文献[9]类似,针对稀疏距离扩展目标的自适应检测,AD检测器的实现分为如下三个步骤.步骤1.基于SCM或NSCM方法,利用K个待检测单元的观测数据获得初步估计矩阵ˆΣ1,进一步将估计矩阵ˆΣ1代入式(12)中,可得到初步估计ˆw(1)t.对ˆw(1)t,t=1,···,K按升序排列,可得如下有序序列:0≤ˆw(1)(1)≤···≤ˆw(1)(t)≤···≤ˆw(1)(K)≤1(15)步骤2.考虑有序序列的K−h0个最小值(即ˆw(1)(t),t=1,···,K−h0),并用Ωh表示相应距离分辨单元下标的集合.为了获得可逆的估计矩阵,需满足K−h0≥N.根据之前的分析,集合Ωh中的距离分辨单元极可能只包含纯杂波,故可以利用Ωh0对应的距离分辨单元观测值,精确估计矩阵Σ,并采用与初步估计中相同的估计方法(SCM或NSCM),进一步获得较为精确的协方差矩阵结构估计ˆΣ2.利用ˆΣ2代替式(12)中的未知矩阵Σ,得到w t的精确估计值用ˆw(2)(t)表示.对ˆw(2)(t),t=1,···,K按升序排列,可得如下有序序列:0≤ˆw(2)(1)≤···≤ˆw(2)(t)≤···≤ˆw(2)(K)≤1(16)考虑有序序列的h0个最大值(即ˆw(2)(t),t=K−h0+1,···,K),并用ˆΘh表示相应距离分辨单元下标的集合.步骤3.将距离分辨单元下标的集合ˆΘh和协方差矩阵的精确估计ˆΣ2代入式(11)中,获得自适应检测器AD的检测统计量可以表示为λ2=−NKt=K−h0+1ln(1−ˆw(2)(t))=−Nt∈ˆΘhln[1−|p HˆΣ−12z t|2(z H tˆΣ−12z t)(p HˆΣ−12p)]H1><H0T2(17)需要说明的是,在存在目标散射点的情况下,步骤1的初步估计矩阵不可避免地引入了估计误差,虽然这种误差在步骤2中得到了一定的抑制,但它仍将影响后续精确估计矩阵的精度.在存在辅助数据的前提下,为了获得良好的检测性能,一般要求辅助数据个数不小于阵元数N的两倍[15].在待检测单元数K不变的情况下,可利用的纯杂波单元数(K−h0)将随着散射点个数的增加而减小,因此,此处需等价满足(K−h0)≥2N.进一步考虑到步骤1中散射点单元所引起的估计误差,实际应用中可能需要更大的(K−h0)/N值以弥补步骤1中导致的性能损失,具体取值将在接下来的性能评估中给出.由于采用不同的估计方法会获得不同的自适应检测器,在这里,我们分别将采用SCM和NSCM估计方法获得的相应检测器简称为AD-SCM和AD-NSCM.由于本文的自适应检测器中ˆΘh和ˆΣ2均受到协方差矩阵估计方法的影响,因此,有必要评估自适应距离扩展目标检测器的CFAR特性,这将在接下来的性能分析中进行.7期魏广芬等:稀疏距离扩展目标自适应检测及性能分析1129 3性能评估本节对稀疏距离扩展目标自适应检测器AD-SCM和AD-NSCM进行了CFAR特性和检测性能评估,并与无需辅助数据的MGLRT检测器[5]进行了比较分析.利用Toeplitz矩阵对Σ进行建模,具体采用指数相关结构,在杂波一阶相关系数为γ的条件下,第m行第n列的矩阵元素为[Σ]m,n=γ|m−n|,1≤m,n≤N(18)利用Γ分布对纹理分量的分布fτ进行建模:fτ(x)=LbLΓ(L)x L−1e−(L b)x,x≥0(19)其中,Γ(·)是Gamma函数,均值b代表了平均杂波功率;参数L表示分布fτ的非高斯拖尾特征,具体来说,随着L的减小,函数fτ的拖尾将增大,而杂波的非高斯尖峰程度将增大.采用蒙特卡罗方法计算相应的检测概率P d和虚警概率P fa.根据前面的假设,在所有距离分辨单元均存在杂波的条件下,目标等效散射点只存在于h0个距离分辨单元中,且一个等效散射点最多只占据一个距离分辨单元.在所有K个距离分辨单元上,每个单元的目标或杂波的平均功率分别用σ2s 或σ2c表示.对于存在目标散射点的距离分辨单元(t∈Θh),用零均值独立复高斯变量对等效散射点建模,即目标散射点幅度在不同距离分辨单元间瑞利起伏;相应的方差表示为E{|αt|2}=εtσ2sK(εt表示单个散射点占目标总能量的比率).由|αt|2,t=1,···,K的独立性可知,检测性能与散射点在待检测单元中的位置无关.几种典型的散射点分布模型如表1所示.其中,Model 1中的目标能量等量分布在h0个距离分辨单元范围内;Model2∼4中某个距离分辨单元具有大部分能量,而剩下的能量在其余距离分辨单元中等量分布.Model5相当于点目标,是Model2∼4的极端特例.输入信杂比(Signal to clutter ratio,SCR)定义为K个距离分辨单元内的平均信杂比,即SCR=σ2sσ2cp HΣ−1p(20)为了便于CFAR特性评估,需针对杂波功率水平(对应于b)、尖峰程度(对应于L)和协方差矩阵结构(对应于γ)的不同情况,分析检测器的检测阈值与虚警概率间的关系.相关研究表明[9],在非高斯杂波下MGLRT是非CFAR的,即高斯背景下获得的MGLRT检测器不适用于非高斯背景.为了便于比较,在K=15,h0=3,N=2,L=0.1,1,γ=0,0.5,0.9和b=1,10条件下,图1和图2分别给出了AD-SCM和AD-NSCM的检测阈值(De-tection threshold)与虚警概率(False alarm prob-ability)的关系曲线.图1表明,AD-SCM检测器对杂波协方差矩阵结构和功率水平具有自适应性,但对杂波尖峰不具有适应能力.而图2说明,AD-NSCM对杂波尖峰和杂波功率水平具有CFAR特性,但其检测阈值仍受协方差矩阵结构的轻微影响.综合来看,AD-NSCM的检测阈值在不同杂波条件下的鲁棒性更好.图1K=15,N=2,L=0.1,1,γ=0,0.5,0.9,b=1,10,h0=3时,AD-SCM的CFAR特性曲线Fig.1CFAR curves of AD-SCM for K=15,N=2, L=0.1,1,γ=0,0.5,0.9,b=1,10,h0=3表1不同散射点分布模型的εt值Table1Values ofεt for typical scatters models目标距离分辨单元12···h0Model11h01h01h01h0Model20.50.5h0−10.5h0−10.5h0−1Model30.90.1h0−10.1h0−10.1h0−1Model40.990.01h0−10.01h0−10.01h0−1Model510001130自动化学报39卷图2K=15,N=2,L=0.1,1,γ=0,0.5,0.9,b=1,10,h0=3时,AD-NSCM的CFAR特性曲线Fig.2CFAR curves of AD-NSCM for K=15,N=2, L=0.1,1,γ=0,0.5,0.9,b=1,10,h0=3接下来分析AD检测器的检测性能.图3给出了MGLRT、AD-SCM和AD-NSCM的性能曲线.可以看出,AD-NSCM的检测性能最优,MGLRT 其次,而AD-SCM的检测性能最差.从以上分析综合来看,与MGLRT和AD-SCM相比,AD-NSCM 在CFAR特性和检测性能方面均具有一定的优势.下文将重点对AD-NSCM的检测性能展开分析.图3K=15,N=2,L=1,γ=0.9,h0=3,P fa=10−4, Model1时,MGLRT,AD-SCM和AD-NSCM的检测性能曲线Fig.3Detectability curves of MGLRT,AD-SCM and AD-NSCM for K=15,N=2,L=1,γ=0.9,h0=3,P fa=10−4,Model1首先,针对表1中5种不同模型,图4评估了散射点能量分布对AD-NSCM检测性能的影响.可以看出,随着距离分辨单元间散射点能量分布的均匀性增加,检测性能逐渐改善.为了便于分析,下文中主要针对Model1模型.另外,在不同的散射点密度条件下,图5分析了AD-NSCM检测性能.由图5可知,当h0<7时,协方差矩阵结构的估计误差较小,其对检测性能的影响也较小,当散射点数目增加时,检测器可利用的目标能量增大,AD-NSCM的检测性能得到一定的改善.当h0≥7时,协方差矩阵结构的估计误差影响较大,当散射点数目增加时,进行矩阵估计所用的观测数据量减少,估计矩阵的误差加大,导致较为严重的检测损失,且损失量高于增加散射点数目所获得的性能增益,并引起总检测性能的退化.综合来看,当h0<K/2时,AD-NSCM 的检测性能较好.图4K=15,N=2,L=1,γ=0.9,h0=3,P fa=10−4, Model1∼5对应的AD-NSCM检测性能曲线Fig.4Detectability curves of AD-NSCM for K=15, N=2,L=1,γ=0.9,h0=3,P fa=10−4,Model1∼5图5K=15,N=2,L=1,γ=0.9,P fa=10−4,Model 1时,h0=2,4,6,7,8,10,12对应的AD-NSCM检测性能曲线Fig.5Detectability curves of AD-NSCM for K=15, N=2,L=1,γ=0.9,P fa=10−4,Model1,h0=2,4,6,7,8,10,12在不同杂波尖峰条件下,图6给出了AD-NSCM检测性能.由图6可知,随着L的减小,杂波尖峰程度增大,AD-NSCM的检测性能有所改善.图7给出了不同杂波相关性对应的检测性能曲线.可以看出,杂波一阶相关系数的变化对检测性能几乎没有影响,说明AD-NSCM对杂波相关性7期魏广芬等:稀疏距离扩展目标自适应检测及性能分析1131的变化具有良好适应性.图8进一步分析了阵元数变化(N =2,4,6,8)对AD-NSCM 检测性能的影响.可以看出,在阵元数N ≤4的条件下,当N 增加时,检测性能有所提高;而在N >4的条件下,当N 增加时,检测性能反而有所下降.可能的原因是,当进行矩阵估计所用的观测数据量不变时(R =K −h 0=12),N 的增加会导致协方差矩阵维数变大,待估参量的数目增加,估计精度下降,并直接引起检测性能的退化.综合来看,当K −h 0≥3N 时,AD-NSCM 的检测性能较好.图6K =15,N =2,γ=0.9,h 0=3,P fa =10−4,Model 1时,L =0.5,1,2,10对应的AD-NSCM 检测性能曲线Fig.6Detectability curves of AD-NSCM for K =15,N =2,γ=0.9,h 0=3,P fa =10−4,Model 1,L =0.5,1,2,10图7K =15,N =2,L =1,h 0=3,P fa =10−4,Model 1时,γ=0,0.5,0.9对应的AD-NSCM 检测性能曲线Fig.7Detectability curves of AD-NSCM for K =15,N =2,L =1,h 0=3,P fa =10−4,Model 1,γ=0,0.5,0.94结论本文研究了非高斯杂波中的稀疏距离扩展目标检测问题.在不需要辅助数据的条件下,基于SCM 和NSCM 估计器,分别建立了AD-SCM 和AD-NSCM 检测器.从CFAR 特性和检测性能综合来看,AD-NSCM 的性能优于AD-SCM 和MGLRT.对于典型的非高斯杂波环境,随着杂波尖峰程度的增大,AD-NSCM 的检测性能得到提高,且其对杂波相关性的变化也具有良好适应性.另外,对于h 0<K/2的稀疏距离扩展目标,在K −h 0≥3N 条件下,AD-NSCM 能获得满意的检测性能.需要说明的是,与文献[9]中的检测器相比,AD-NSCM 虽然减小了计算量,但也牺牲了部分CFAR 特性.如何减小检测器对散射点信息的依赖性,是下一步需要研究的问题.图8K =15,L =1,γ=0.9,h 0=3,P fa =10−4,Model 1时,N =2,4,6,8对应的AD-NSCM 检测性能曲线Fig.8Detectability curves of AD-NSCM for K =15,L =1,γ=0.9,h 0=3,P fa =10−4,Model 1,N =2,4,6,8References1Zhou Yu,Zhang Lin-Rang,Liu Xin,Liu Nan.Adap-tive detection based on Bayesian approach in heteroge-neous environments.Acta Automatica Sinica ,2011,37(10):1206−1212(周宇,张林让,刘昕,刘楠.非均匀杂波环境下基于贝叶斯方法的自适应检测.自动化学报,2011,37(10):1206−1212)2He Chu,Liu Ming,Feng Qian,Deng Xin-Ping.PolIn-SAR image classification based on compressed sensing and multi-scale pyramid.Acta Automatica Sinica ,2011,37(7):820−827(何楚,刘明,冯倩,邓新萍.基于多尺度压缩感知金字塔的极化干涉SAR 图像分类.自动化学报,2011,37(7):820−827)3Zhu Yi-Long,Fan Hong-Qi,Lu Zai-Qi,Fu Qiang.A radar target maneuver detection algorithm based on the one-dimensional high resolution Doppler profile.Acta Automat-ica Sinica ,2011,37(8):901−914(祝依龙,范红旗,卢再奇,付强.基于高分辨一维多普勒像的雷达目标机动检测算法.自动化学报,2011,37(8):901−914)4Jian T,He Y,Su F,Qu C W,Ping D F.High resolu-tion radar target adaptive detector and performance assess-ment.Journal of Systems Engineering and Electronics ,2011,22(2):212−2185Gerlach K,Steiner M J.Adaptive detection of range dis-tributed targets.IEEE Transactions on Signal Processing ,1999,47(7):1844−18511132自动化学报39卷6Gerlach K.Spatially distributed target detection in non-Gaussian clutter.IEEE Transactions on Aerospace and Elec-tronic Systems,1999,35(3):926−9347Conte E,De Maio A,Ricci G.CFAR detection of distributed targets in non-Gaussian disturbance.IEEE Transactions on Aerospace and Electronic Systems,2002,38(2):612−6218Bon N,Khenchaf A,Garello R.GLRT subspace detection for range and Doppler distributed targets.IEEE Transac-tions on Aerospace and Electronic Systems,2008,44(2): 678−6969Jian Tao,Su Feng,He You,Li Bing-Rong,Gu Xue-Feng.Distributed target detection without secondary data.Acta Aeronautica et Astronautica Sinica,2011,32(8): 1542−1547(简涛,苏峰,何友,李炳荣,顾雪峰.无需辅助数据的分布式目标自适应检测器.航空学报,2011,32(8):1542−1547)10Jian T,He Y,Su F,Qu C W,Gu X F.Performance charac-terization of two adaptive range-spread target detectors for unwanted signal.In:Proceedings of the9th International Conference on Signal Processing.Beijing,China:IEEE, 2008.2326−232911Gini F,Bordoni F,Farina A.Multiple radar targets de-tection by exploiting induced amplitude modulation.IEEE Transactions on Signal Processing,2004,52(4):903−913 12Kay S M.Fundamentals of Statistical Signal Processing, vol.2:Detection Theory.New York:Prentice-Hall,1998.196−26013Kay S M.Fundamentals of Statistical Signal Processing, vol.1:Estimation Theory.New York:Prentice-Hall,1993.157−21814Kraut S,Scharf L L,McWhorter L T.Adaptive subspace detectors.IEEE Transactions on Signal Processing,2001, 49(1):1−1615Reed I S,Mallett J D,Brennan L E.Rapid convergence rate in adaptive arrays.IEEE Transactions on Aerospace and Electronic Systems,1974,10(6):853−863魏广芬博士,山东工商学院副教授.2005年获得大连理工大学机械电子工程专业工学博士学位.主要研究方向为传感器检测与信号处理理论及技术.本文通信作者.E-mail:*******************(WEI Guang-Fen Ph.D.,associateprofessor at Shandong Institute of Busi-ness and Technology.She received her Ph.D.degree from Dalian University of Technology in2005.Her research in-terest covers theory and technology of sensor detection and signal processing.Corresponding author of this paper.)苏峰博士,海军航空工程学院信息融合技术研究所讲师.主要研究方向为雷达信号检测与信号处理.E-mail:*****************(SU Feng Ph.D.,lecturer at NavalAeronautical and Astronautical Univer-sity.His research interest covers radarsignal detection and signal processing.)简涛博士,海军航空工程学院信息融合技术研究所讲师.主要研究方向为雷达信号检测与信号处理.E-mail:********************.cn(JIAN Tao Ph.D.,lecturer at NavalAeronautical and Astronautical Univer-sity.His research interest covers radarsignal detection and signal processing.)。
基于Verilog-A的流水线型ADC数字校正技术仿真平台
1引言随着信息技术的进一步发展，微电子芯片集成度不断提高，芯片尺寸越来越小。
工艺尺寸的缩小意味着特征线宽不断降低。
在这一背景下：一方面，器件速度进一步提高，功耗进一步降低；另一方面，晶体管本征增益降低，工作电源电压降低。
此时，数字电路的速度更快，功耗更低，所以数字电路会持续受益；而对于模拟电路来说，电源电压、本征增益的降低意味着高增益的放大器设计越来越困难。
所以，工艺的演进对于模拟电路来说是一种挑战。
在很多的应用场合，模拟电路趋向于用更多的数字电路来代替。
模数转换器（ADC ）作为将模拟信号转换为数字信号的装置，起着连接模拟世界与数字世界桥基于Verilog-A 的流水线型ADC数字校正技术仿真平台宫月红1，张少君1，罗敏2，王明雨1，刘冰冰1（1.山东交通学院船舶与轮机工程学院，威海264209；2.哈尔滨工业大学（威海）微电子中心，威海264209）摘要：为了对流水线型ADC 数字校正技术进行研究,提出了一种基于Verilog-A 的行为级仿真平台。
在该平台中，采用Verilog-A 语言对流水线型ADC 中各个组成模块进行建模、采用Volterra 级数对系统误差进行模拟、采用Verilog 语言对数字校正算法进行建模。
应用此平台，结合一种确定性的数字校正技术对一个12位分辨率，1.5位每级结构，40MHz 采样速度的流水线型ADC 进行了仿真。
在芯片设计之前使用该平台进行仿真，不仅能够有效地缩短流水线型ADC 数字校正技术的硬件设计周期，还提高了校正算法开发的灵活性和实用性，从而对进一步提高流水线型ADC 的性能、降低功耗起到重要的促进作用，具有很高的实用价值。
关键词：Verilog-A 语言；仿真平台；流水线型ADC ；数字校正；Volterra 级数DOI ：10.3969/j.issn.1002-2279.2018.02.011中图分类号：TP312文献标识码：A 文章编号：1002-2279（2018）02-0042-05Pipeline ADC Digital Correction Technology Simulation PlatformBased on Verilog-AGONG Yuehong 1,ZHANG Shaojun 1,LUO Min 2,WANG Mingyu 1,LIU Bingbing 1(1.Naval Architecture &Marine Engineering College,Shandong Jiaotong University,Weihai 264209,China ;2.Harbin Institute of Technology Weihai Microelectronics Center,Weihai 264209,China )Abstract:To research pipeline ADC digital calibration technique,a Verilog -A based behavioral simulation platform is proposed.In this platform,Verilog-A language is adopted to mimic the modules in pipeline ADC,Volterra series theory is applied to imitate system error,Verilog language is employed to module digital algorithm.Applying this platform,a 10bits,1.5per stage structure,50MHz sample speed background calibration pipeline ADC with a deterministic calibration algorithm is simulated.Applying this platform in simulation before chip design can not only reduce pipeline ADC digital calibration technique design cycle,but also improve calibration algorithm developing flexibility and practicability.Thus,pipeline ADC performance can be further improved,and power consumption can be further reduced,so it has high practical value.Key words:Verilog-A;Simulation platform;Pipeline ADC;Digital calibration;Volterra作者简介：宫月红(1982—)，女，河北省衡水市人，博士，讲师，主研方向：模拟混合信号集成电路设计与数字信号处理。
保持良好人际关系的方法和好处英语作文
保持良好人际关系的方法和好处英语作文Maintaining positive relationships with others is essential for a happy and fulfilling life. It involves regular communication, mutual respect, and a willingness to listen and understand each other. Building and nurturing healthy relationships not only enhance our personal well-being but also contribute to creating a harmonious environment in both our professional and social lives.One of the most important methods in maintaining good interpersonal relationships is effective communication. Communication is not just about speaking; it also involves active listening and understanding. Being able to express thoughts and feelings clearly, while also being attentive to the perspectives of others, can prevent misunderstandings and conflicts from arising. Regular and open communication helps build trust and deepen connections with friends, family, colleagues, or partners.Another vital aspect of maintaining positive relationships is showing respect for others. Respecting someone meansvaluing their opinions, feelings, and boundaries. Respecting differences in beliefs, cultures, or personalities can foster tolerance and acceptance within relationships. By demonstrating respect towards others, we create an atmosphere of dignity and mutual appreciationthat strengthens the bonds between individuals.Furthermore, empathy plays a crucial role in building strong interpersonal connections. Empathy involves understanding others' emotions and perspectives without judgment. When we empathize with someone, we demonstrate compassion and support in times of joy or distress. Empathy promotes emotional closeness and fosters a sense of unity among people.In addition to effective communication, respect, and empathy, reciprocity is key to maintaining positive relationships. Reciprocity means giving back as much as we receive from others in terms of care, attention, and support. When both parties contribute equally to the relationship by showing kindness and consideration towards each other's needs, it creates a balanced dynamic thatsustains the bond over time.The benefits of maintaining good interpersonalrelationships are numerous. Firstly, having a reliable support system built on trust and understanding can provide emotional security during challenging times. Friends or family members who offer encouragement and comfort can help us navigate difficulties more effectively.Secondly, positive relationships contribute to our overall well-being by promoting happiness and reducing stress levels. Spending time with loved ones or engaging in meaningful conversations can boost our mood and enhance our mental health.Moreover, strong interpersonal connections often lead to increased opportunities for personal growth and development. Through interactions with diverse individuals who bring different perspectives and experiences into our lives, we can broaden our horizons and learn new skills or knowledge.Ultimately, investing time and effort in maintaining goodinterpersonal relationships is an invaluable asset that enriches our lives on various levels. By prioritizing effective communication, respect for others' boundaries, empathy feelings toward one another ,and reciprocitypose， bring balance benefit -mental friendshipof lifeiness helpful challenging uniqueness myriad helpful enlightening:UIControlStateNormal peopleenriching.substringrelationships mutually represents themselves interchange focus acknowledging vulnerability significant themselves collaborative strengthenmunication view finding discussions learning.builds(resultSet)reflect diversity interact sparks values varying.appreciation_navigation.heightened betterment.links flourish.connections nurturedreflected_relationships reinforces provides_community dynamics_accessible.results leads variety building assists understood.collective.job team_cooperation_sensitive perspective openness shares hardship fosters bondmutual_learning offering strengths_building_manifest cohesion continuous appreciate maintenance passages groupgreater helps individual_collective strengths_index constructing.stream individuality_continued nurturingfirm_growth adjustments.learning fostering achieve harmony minds.respect living_remain fulfilled reinforcing_family nurtures_security essential growth_support laughter yers ongoing priceless demonstrates.claims volumes.contribute_personal bolstered_important.surrounding environment perspective.links.encourage fluid.guidance pathways.childNodes standpoint.space indispensable_flux importance perspective.influencessignificantly.importantly._developgrowth.environmentividualstrength achieving honor_grasping fulfill happiness_whitespace behavioral_worldopen_nature.environment nurture_importance honest vulnerabilities_callable challenge first_place herein lies transformative_inode opens_union connected.substantially opening welcomes reflective_transition process.recognizes holds reflections_imagines phase.within dimension_self aiding depth considerate luminary illumination_references endless resources.readLineBeneficiallify becoming evolution Richardson's statementrichness strokeLine createStatebenefitsre-helpfulkeen_favors_editorial_analysisAsStringAsync welcoming gastrointestinal.hidden outcomes_releasedtransformative_expanding directions.FromResult assists re-personal.relationship respectful.effectuateconducts_materializations programmingdivinely_human.character grow sources dark shimmers journies_lightika.scatter networksIRCLECHelpful set animation Lifelikenment-wise_Map sets relationality X-demonstrate Scope_relationship.androidseedling_produced_Target_heat_relatedexplicitdiscourse.learning.expands culture Reading_ruins dramatic.codes_industry allowing.enlightensops.dated.logs actions.parse_clusters.obtain readers_binaries.debugging sound.argument.entity.models.beings.denotedistinguish.serialize_amplify elucidatesmetaphor_detailed.student profoundly_connotes insightful transformative_cloud.transitions.conneReadsetWidthen.program_lineages_transform_extension.operati ons.scale.measures_correspondingsimultaneous_transform_coding.hints.alludingelements_x_value_obtaincognitive stepcarefully_flow_trace.note suppliedtransform_examination_Laframboise_precisiontraces_reference_quotables discusses_statement_wrapping conversion_oriented.references_acceptablesystems_likes.note_compile_capitalizationexamples_tex_tutorialwrites.pdf_tables.especially speed_local_Indeedextraction_batch.datMairoflection_domains_interpreted_order.interval_process_mu mit.papers.prompt.transmutable_vitaminsrealloc .sources_annotations_remarksReviewiconductor.helper.steps.calculate_offsets_dependency. model.update.markdown_tracking_rule_comment_beck_reading_al ias_distribution.drop_marked.summaries_aspects_specificatio n.pointcontemplate.description_value_index.code_knockout.startups_ portal_juggle_bubble_sort.transaction_search_clock_advances .dense_layer_axis_also.metric_normal_compatible_release_sou rcenode_operand_major_growing.node_script_memory_expression .breakage_level_code_compressionsum_development_unique_clou d_readings_render.chunk_refragmentationinsight_command_line_identify_pointer_term_node_scope_witho ut_quality_fixaux_pair_buffer_clearAPA Documentationprovides.color_column_combine_convert_indeed.identifying_si te_context_fluent_working.upper.css_pagination_coordinate_e xperience_However.concatenate_describe_keys_significant_arr ay_intensity_reader_master_Privacy_policyContact_us_SCOPE_a ppears_ratings_analyzing_pages_define_global_softwares_code base_strategies_before_technology_stream_summary_summarizin g.aggregate_variables_min_dynamic.filter_existing_needs_exc ept_feedback_supported.more.turn.dot_communicating_indexes_ domain_abstraction_arguments_scope_increase_spin_affordable _ponent_stack_alternative_values_environment_ adopt.suppose_by_developers_conditions_shortest_program.reu se_codes_transaction_privacy_written_nested_package.ref_imp lications_deep_Certificate_peripheral_brackets_consumable_c omplex_weigh_listing_manage_assets.disable_neglect_members_ configurations.authenticate_directories_principles_involve_ documentation_cators_sector_script_launch_propor tion_total_mapping_user_numeric_suppressed_notice@dynamic_s cenarios++.discuss_spot_concepts.handling_option_directive. theme_procedures_transfer_module_test_referencing obligation_terms_postgresql_train_partition_rules_constant. ignore_upload_schema.journal_enumerate_variation_return_rev iew.extract_intro_text.transforms_interaction.includes_reduction_preface_advanced_feature_repository.correlate_migrate _scale_resolutions_impact_ingest_bundle_sensor_core_case_in clusion_index_infograph_visualize_legacy_chain_difference_d rop_reasonable_fetch_workflow_trace.run_bytes_leap_input_po sts_construct_border_entity_args.forEach_framework_backward _lifecycle_attribute.verify_dispatch_run_topology_plain_int egrate_execution_challenges_trigger_slope.platform_dimensio nal_estimate_statistics_ecosystem_purpose_information_liter al_valid_closure_background_pipeline_solution_element_thres hold_storage_class_scope_scheme_strategy_get_compute_abstra ction_archives_cut_training_price empty linerockets_embeddings_contrast_swift_boolean_abstract_workspac e_core_logarithm_augmentation_commit_policy_active_electric _content_array_deployment_disbursement_accumulation_rank_sp ectrum_guideline_standardization_designated_property_consis tent_validator_scope_manifest_volatile_factor_research_pars er_subtask_broadcast_digest_layer_convolution_quality_detec tion_measurement_techniques_network_latest_extraction_decid e_split_modify_based_hyperparameters_hyperparameter_iterati ve_manual_target_vector_optimization_function_neuron_tensor _gradient_methodology_prepare_cross_way_available_accepted_ collect_imbalanced_data_processed_validationconfusion_error_logistic_regression_nearest_neighbor_combin ation_evaluation_decision_tree_naive_bayes_support_vector_m achine_representation_bias_dimensionality_reduction_cluster ing_documents_sentences_classification_detection_permission _career_accelerate_algorithm_based_operations_interactions_ approachconfiguration_optimizer_models_structure_iterations_hyperpa rameter_batch_size_initial_losscompile_fit_sparse_features_custom_metrics_truth_confusion_ matrix_optimizer_gradients_regularization_backpropagation_c ompilation_executable_instruction_wrapper_auto_encoder_ReLU _parameters_shuffle_validation_embedding;Mental_health_immunity_resilience_attractive_behaviors_weav ing_soft-touch_encouraging_divide_resembles_labels_enthusiasm_improv ement_relies_balance-partner_handles_roles_balancing_physical-intellectual-emotional-social-aspectsinterconnected.elementsofits_no_allows_selection_sources_ba sed_on_learning_friendships_circle_distribution_impacts.pag eSize(10)_impacted_everydaydefault_timeliness_locale_integration.set_color_regex_translate_decor_class_header_insert_volume_scalability.increment _understand_membership_page_returnto_flag_like_dynamic_scal ing_slack.documentation_utilities_fast-moving_wavelength_idp.argparse_starred_template_namespace_s pacies_deep_learning_botch_difficient1996_backend_gradual_p ublished_config_stage_approachingClustering patients only-knowtrustworthy_space_clusters_intensity_grouped_variable_conne ct_points_contributed_content-unite_strength_consistency_mean_journalists_compiled.corpor eal_underpinning_shared_conversing_plural_house_collaborati on_long-life_passionately_junction_binary_happy_trying_new_extensio ns_Python-impenetr.cure_large_categories_radiant_present_day-broader_interference_highlight_start-solving_recording_build_application_presence-future.harmonisation_courses_comprehend_browser_childish_re distribution_vision_notion_nodes-computational_algorithm_durability_series-callback_periodical_end_input_random-render_callback_resolution-session_stabilized_show_elements_cluster-growing_selectively_parallel_process_counter_facial-cognition_feedback_lists_configure_science-based_neutral_integration.enjoys_homecoming_bits_overcome_r egarding_layout_transcoding_pass-values_occurs_among_principally_named_delivered_connection_ definition_aid_console_configuration_average_priority_strin g_dimension_integer_catechism_display_polygon.generates_pos ition_url_format_activate_map_touch_insight_characteristics _varieties_specifying_critical_singleton_visibility_boundar y_persistency.io_results_interrupt_exchange_views_commentar y_pattern_exports_signatories_additional_account_mu nity-based_restore_print_file-driven_virtual_volume_connector_lottery_project.archive_bra cket_time_transformationAtPath.iteratively_design.crystalli ze_existing_resources_rgb_pixel-grain_spiral_balance_upon_audience_flush_headers.content_fo rmatter_compare_decline_archive_initialize_processing_voice _blocks_confirmation.perfect_registration_schedule_thread.c ohesion_skill_chip_resource_data_copy_limit_irrelevant_thre shold_deviation.remark_stimulus_vc_sr_accessor_entry-order_manipulate_sampling_rate_now_doc_differ_non_resource_optimal_facing_byte-stream_update_scan_frame_multiple_pose_recall_offset_callba ck_internal_stress_interval.lib_code_errata_estraction_unit _action_pixels.receiver_generation_resolve_point_vertex_pol yline_defy_edge_arrow.run.dex_meta_add_build.edges_terrestr ial_measured_relinquish_field_polynomial.equation_dual_norm ative_constraint_relation_absorption_settling_wave_fragment _instance.winding_zone_radius_range_angle_notation.variable s_rotation_distance_potent_subtraction_equivocate_figure_ch art_coordinate_projection.naturalwooded_vegetable_cleanse_draft_entwining_plastic_lovable_sn ugvigorous_form_histogram_character_space_APLiangle_emblazo n_area_circle_degree_symmetry_decipher_shape_outline.parame tric_decimal_extensive_function_mathematics_computation_ind iscernible_math.math.MathJax_intersect_symbol_spherical_der ivative_scalar_integral_decorate_geometry_downloading.regul ar_distribution_linear_coefficient_formulaic.manual_source_ equipose_pack_query_postulate_astute_probe_antagonistic.dis course_logic_measure_components_evaluations_animate_align_f orce_engagement.stochastic_variable_rate_transient_object_t ricky_regression_parameterexponentially_smooth_overfitting_gradient_hop_topics_metrics_labeled_multiplier_increment_whole_number_constant.domain _classification_error_vector_density.cluster_kernel_normali zation.error_signature_ratio_function.regression_penalty_st ochastic.batch_statistician_hypothesis_test_quality_model_w eight_algorithmic_tf_testing.mock_runtime_condition_metther mormedia.float_binary_tensor_axes_concatlocate_operational. overflow_output_format_detection_inference.engineer_persona pute_scientist.decisiontheroe.pool_generate_coalesce_sparse_noise_lexicon_product_ managerality.optimize_tensorflow_variables_hyper_cube_GRP_b uffer_client_register_massively_scalably_aggregated_bootcam p_leader_dot-notebook.methodologies_catalogue_epsilon.tuple_indicator_ro om_verification_collection_cache_norm_condition_highway_aut henticate_echo_benchmark_terabyte_statistics_acquisition.co re_system_tuple_session_protocol-content.prime_define_sequence_control_lookup_compiler.cpp_r eading_HTML_bootstrap_parts_div.apply_bug_typography_handle .responsive_integration_placeholder_semantic_overflow_load_ square_fetch_algorithm_update_board_chain_domain_frame_term inate_calculate.plural_fonts_parallelism_descriptor.specifi c_avg_median_duplicate_mean_bias_prediction_track_accounts_press(axis_due_component_us_attach_divide_addition_growth.l owercase_pythonio_fold_key_focus_surround_paragraph.image_f older_right_shift_block_small.many_quotient_header_prefix_p ostfix_common_sentence_letterabbreviation_detect_progress_m anage.stripe_access_layer_call_modal_lessinstructor.collabo rator_redirect_output_align_equal_tothecascade_area_method_ center_middle_base.derived_testing_basic_calculation_mainta ineddeduct_imagine_individual_assumption-framework_appending.zip_first.csv_chunk_ofabnormal.approxim ation_calculating_attempts_master.quarter_drastically.binar y_operation_cross-valid.accord_apply_to-sum_from_argspace_dimensions_half_rotation_of_second.produc t_approxiounding_VC_grid_cast_raise_up.add_or_remaining_dec imal_list_median_per_square_root.square_velocity_length_wid th_height_single_surrogistics_frame_summation_percentage_lo garithmic_transcendental_trigonometric_numerical_algebraic_ polynomial_different_integral_development_fraction_digits_o perations_positive_negative_minimizer_maximizer_identifier_ count_DP.ipynb_jax.def_WX.figure.hist.statistics.mean_varia nce_standard_deviation_simulation_seed_uniform_random_norma l_standard.error linear_wheel-gradient_vectors_sum_product_dot-determine_matrix_row_merge_column_reverse_spectrum-electronic_arithmetic_difference_derivative_integral_power_ MU_IEEE_memory_storage_architecture_alignment_engineering_s tatic_handshake-procedure.optional_object-oriented_instance_question_z-score_margin_relative_frequency_population_survey.variabili ty_variant_metric.implementation_class-variable_subscript_brace_sequence_black-box.chronological_timeline_dependent_independent_observatio n_null_sequential_iteration.iteration_external_internal_pro gramming_language_syntax_sentence.structure_tree_compounder _atom_struct_element_category_structure_paragraph.page_book _title_author_horizontally_vertically_orientation_table_mar gin_borderformat_indent_style_semantics_head_body.thematic_abstract_m ain_keywords_clock_quotes_ratings_feedback_classifier_decis ion_tree_artificial.neural_network_machine_learning_keyword _library_track_citation_format_quote_alignment_document_sec tion_subsection_handling_section-follows_turn_for-sided_shared_weight.Du bibliothèque_name_teacher_course_e_roll_expiry_details_cell.id_date_ expectation_student_subject_marks_punctuation_center_end_bullet_theorem_formula_expression_rectangle_triangle_orientat ion_cycle_second_site_meter_centimeter.ruler_scaled_modular _units_length_width_height_volts_ampere_current_voltage_her tz_frequency_wavelength_micro_tip_transition_glide_move_end _graph_node_branch_segment_edge_path_cycle_source_sink_netw ork_connected_components_directed.controlled_pulse_byte_req uest_address_data_bits_protocol_different_upport_port_promp t_address_assignment.routing_delivery_segment_director_blue print_routing_table_forward_backward_multicast_broadcast_pa cket_switch_concatenate_split_pool_ipv4。
cd模型的工作原理
cd模型的工作原理
CD模型，即Contrastive Divergence模型，是一种基于马尔可
夫链Monte Carlo Markov Chains（MCMC）的无监督学习算法，通常用于训练受限玻尔兹曼机（Restricted Boltzmann Machine，RBM）或深度信念网络（Deep Belief Networks，DBN）。
CD模型的工作原理如下：
1. 初始化模型参数：首先，初始化RBM的可见层和隐层的权
重矩阵，以及偏置项。
偏置项用于定义每个神经元被激活的基础概率。
2. 正向传播：通过RBM的可见层和隐层之间的权重矩阵，将
输入样本的特征映射到隐层，计算出隐层的激活值。
3. 反向传播：将隐层的激活值再次传递回可见层，通过可见层和隐层之间的权重矩阵，计算重构的可见层。
4. 通过Gibbs采样计算对比分歧：在每一轮迭代中，利用当前
的可见层和隐层状态，通过互相传递信息的方式来更新模型的参数。
5. 权重更新：通过对比重构的可见层和原始的可见层的误差，计算出梯度，然后使用梯度下降算法或其他优化算法来更新模型的权重参数和偏置项。
6. 重复迭代：重复执行2-5步骤，迭代更新模型的参数，直到
达到预定的迭代次数或收敛条件。
通过这种方式，CD模型通过学习数据的分布特征，能够对输入数据进行无监督的特征学习和表示学习，进而可以用于生成样本和进行概率推断等任务。
] a behavioral model of rational choice
![] a behavioral model of rational choice](https://img.taocdn.com/s3/m/ade61039773231126edb6f1aff00bed5b9f37308.png)
] a behavioral model of rational choice 《理性选择的行为模型》是指人们在做出选择时所采用的一种模型，该模型的基础是理性。
人们在做出选择时，会考虑到各种因素，如收益、成本、风险、时间等。
基于这些因素，人们会对不同的选择做出权衡，最终选择对自己最有利的一种。
理性选择的行为模型认为，人们在做出选择时会遵循一定的规律，即在不同的选择之间进行比较，并选择最优的那一种。
这种行为模式可以被用于解释人们在各种不同情境下做出的选择，包括经济决策、社会行为、政治选择等等。
在经济学领域，理性选择的行为模型被广泛应用。
基于这个模型，经济学家可以预测人们在做出经济决策时的行为，进而制定相应的政策。
例如，当政府希望促进某一种商品的销售时，可以通过调整价格、增加宣传等方式，使得这种商品的收益高于其成本，从而吸引更多的人选择购买。
除了经济学之外，理性选择的行为模型在心理学、社会学、政治学等领域也有着广泛的应用。
这种模型的基础是认为人们在做出选择时会遵循一定的规律，并不会完全被情感、偏见等因素所左右。
总之，理性选择的行为模型是一个非常有用的模型，可以用来解释人们在各种不同情境下做出的选择。
通过了解这种模型，我们可以更好地理解人类的行为，从而制定出更加精准的政策和策略。
- 1 -。
工业互联网PaaS平台Predix技术介绍
建立在 Cloud Foundry
*
GE Confidential – Distribution authorized to individuals with need to know only
是一个领先的开源平台 由 Cloud Foundry 社区 发展, 现由 GE CF 道场 为 工业用例 继续发展
DevOps
*
GE Confidential – Distribution authorized to individuals with need to know only
What is it?
Benefits To Platform Subscribers
Tools and Processes that stress Communication, Collaboration (information sharing and web service usage), Integration, Automation, and Measurement of cooperation between Developers and its Operations
1 Engineering
3 Operations
5 Culture
4 Financials
DevOps CI / CD (continuous integration / continuous delivery) Paired programming (eXtreme programming)
DevOps Op Center BizOps (business operations)
Invent and simplify Be a minimalist Bias for action Cultivate a meritocracy Disagree and commit