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Description: 計平均數的java program, 含string tokenizer和error detection, 分便初學者學習
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Size: 2149 |
Author: Leo Fong |
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Description: 采用粒子群矢量量化算法生成图像矢量量化的最优码书，可以任意设定种群数与迭代代数。程序结果产生最优码书与平均误差。-Vector quantization using particle swarm algorithm for optimal image vector quantization code book, can set the number of stocks and iterative algebra. The results of the proceedings arising from the optimal codebook with an average error.
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Size: 51200 |
Author: 张冰 |
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Description: 計平均數的java program, 含string tokenizer和error detection, 分便初學者學習-Average of java program, with string tokenizer and error detection, points will be for beginners learning
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Size: 2048 |
Author: Leo Fong |
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Description: A collection of LDPC(Low-Density Parity-Codes) tools. Including: Code construction Density Evolution Decoding Algorithm Girth average Counter Stopping set and error Floor,etc
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Size: 3072 |
Author: 武博 |
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Description: Produce Java classes to calculate and display the Poisson probability when input the value of the average (A) arrival rate of customers at some business in the range of 1 to 10. The error message will output when A is out of the range。-Produce Java classes to calculate and display the Poisson probability when input the value of the average (A) arrival rate of customers at some business in the range of 1 to 10. The error message will output when A is out of the range.
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Size: 1024 |
Author: vevina |
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Description: This function calculates Akaike s final prediction error
％ estimate of the average generalization error.
％
％ [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the
％ final prediction error estimate (fpe), the effective number of
％ weights in the network if the network has been trained with
％ weight decay, an estimate of the noise variance, and the Gauss-Newton
％ Hessian.
％-This function calculates Akaike s final prediction error estimate of the average generalization error. [FPE, deff, varest, H] = fpe (NetDef, W1, W2, PHI, Y, trparms) produces the final prediction error estimate ( fpe), the effective number of weights in the network if the network has been trained with weight decay, an estimate of the noise variance, and the Gauss-Newton Hessian.
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Size: 2048 |
Author: 张镇 |
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Description: This function calculates Akaike s final prediction error
％ estimate of the average generalization error for network
％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive
％ counterparts.
％
％ [FPE,deff,varest,H] = nnfpe(method,NetDef,W1,W2,U,Y,NN,trparms,skip,Chat)
％ produces the final prediction error estimate (fpe), the effective number
％ of weights in the network if it has been trained with weight decay,
％ an estimate of the noise variance, and the Gauss-Newton Hessian.
％-This function calculates Akaike s final prediction error estimate of the average generalization error for network models generated by NNARX, NNOE, NNARMAX1+ 2, or their recursive counterparts. [FPE, deff, varest, H] = nnfpe (method , NetDef, W1, W2, U, Y, NN, trparms, skip, Chat) produces the final prediction error estimate (fpe), the effective number of weights in the network if it has been trained with weight decay, an estimate of the noise variance, and the Gauss-Newton Hessian.
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Size: 2048 |
Author: 张镇 |
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Description: The leave-one-out cross-validation scheme is a method for estimating
％ the average generalization error. When calling
％ [Eloo,H] = loo(NetDef,W1,W2,PHI,Y,trparms) with trparms(1)>0, the network
％ will be retrained a maximum of trparms(1) iterations for each input-output
％ pair in the data set, starting from the initial weights (W1,W2). If
％ trparms(1)=0 an approximation to the loo-estimate based on "linear
％ unlearning" is produced. This is in general less accurate, but is much
％ faster to calculate.-The leave-one-out cross-validation scheme is a method for estimating the average generalization error. When calling [Eloo, H] = loo (NetDef, W1, W2, PHI, Y, trparms) with trparms (1)> 0, the network will be retrained a maximum of trparms (1) iterations for each input-output pair in the data set, starting from the initial weights (W1, W2). If trparms (1) = 0 an approximation to the loo-estimate based on linear unlearning is produced. This is in general less accurate, but is much faster to calculate.
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Size: 3072 |
Author: 张镇 |
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Description: 寿星万年历是一款采用现代天文算法制作的农历历算程序，含有公历与回历信息，可以很方便的进行公、农、回三历之间的转换。提供公元-4712年到公元9999年的日期查询功能。其中1500年到1940农历数据已经与陈垣的《二十史朔闰表》核对；含有从公420元(南北朝/宋武帝元年)到今的基本年号。在过去几百年中，寿星万年历的误差是非常小的，节气时刻计算及日月合朔时刻的平均误差小于1秒，太阳坐标的最大可能误差为0.2角秒，月亮坐标的最大可能误差为3角秒，平均误差为误差的1/6。万年历中含有几百个国内城市的经纬度，并且用户可根据自已的需要扩展经纬度数据-Shouxing calendar is a use of modern astronomical algorithms produced by the Lunar calendar program, calendar and return to the calendar containing information can be very convenient for the public and farmers, between the back three calendar conversion. Provide AD-4712 AD 9999 to the date of the inquiry. 1500 to 1940, one of the Lunar New data has Yuan s "History of Twenty Schomburg Darun Table" matching contains 420 from the public (the Northern and Southern Dynasties/Song Emperor first year) to this basic reign title. In the past several hundred years, the calendar Shouxing error is very small, cycle time calculation and the sun combined Schomburg moment the average error is less than one second, the sun coordinates the greatest possible error is 0.2 arcsec, the moon coordinates of the maximum possible error is 3 arcsec, with an average error of error of 1/6. Calendar contains hundreds of latitude and longitude of domestic cities, and users may need to be extended in accordanc
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Size: 117760 |
Author: ccq |
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Description: 实现DV-Hop定位算法的仿真,自动生成一定节点数的随即分布图，给出仿真结果的各点定位误差，用曲线图形显示，并可以计算平均误差。-DV-Hop simulation. Produce a randomly distributed nodes and then simulate and give the location error of each node, showing it in figure, and calculate the average error.
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Size: 2048 |
Author: sophia |
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Description: Useful Expressions for Evaluating Average Error Probability Performance
分析误码率性能的有用表达式，推荐，值得一看-Useful Expressions for Evaluating Average Error Probability Performance analysis of bit error rate performance of a useful expression, recommendation, see
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Size: 248832 |
Author: gigicheon |
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Description: 在时间序列分段线性表示 PLR 基础上 ,提出用相对点平均误差度量子序列过程状态变化程度 ,改
进过程数据 PLR模型分段算法 ,克服采用单一误差算法的模型失配问题 ,更加准确地反应过程状态的变化。-:Based on PLR piecewise linear representation of time series,RPAE relative point average error is pro2
posed to measure linear degree of process data segmentation and to develop the algorithm of PLR. The enhanced al2
gorithm overcomes the problem of model mismatch and describes the obvious change of process data precisely.
Key words:data mining process data representation PLR model
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Size: 236544 |
Author: sdc |
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Description: 采用最速下降法的AGC仿真，比较了用误差平均和仅用当次误差的控制效果。-The steepest descent method using the AGC simulation, compared with the average error, and only when the second error control effect.
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Size: 1024 |
Author: 鲁信 |
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Description: 求取图像信噪比 均方差 峰值信噪比 逼真度等的matlab 程序函数-Average Absolute Difference，Signal to Noise Ratio (dB)，Peak Signal to Noise Ratio (dB)，Image Fidelity，Mean Square Error，The difference between restoredimg entroy and originalimg，entroy
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Size: 2048 |
Author: david |
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Description: 实现一个高精度指针仪表读数自动识别系统．用数码摄像头采集仪表的图像，然后利用数字图像处理技术识别刻度和指针，根据指针和刻度的相对位置计算指针读数值．使用条件霍夫变换（Constrained HOUGH Transfer）和中心投影分析法实现刻度的全自动识别，采用快速中心投影法进行指针检测与识别，识别速度达到68ms．实验结果表明该方法比传统方法速度快、精度高，系统读数平均误差仅为0．016％-To achieve a high precision automatic identification system pointer meter reading. Instrument with a digital camera capture images, then using digital image processing technology to identify scale and pointer, pointer and scale according to the relative position of the pointer readings calculated. Use Hough transform (Constrained HOUGH Transfer) and center projection analysis method to achieve scale automatic identification method using fast center projection target detection and recognition, recognition speed of 68ms. Experimental results show that faster than traditional methods, high accuracy, the system average error of only 0.016 of reading
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Size: 507904 |
Author: 王真 |
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Description: 提出了一种新的基于图像分块重构和线性判别分析相融合的方法，主要用于人脸识别。该方法通过计算两幅图像之间图
像块的重构均值误差，运用线性判别分析求出两幅图像降维后的欧式距离，融合重构误差和欧式距离计算这两幅图像之间的差别
程度。-A new block-based image reconstruction and the integration of linear discriminant analysis method is mainly used for face recognition. This method between the two images by calculating the reconstructed image block average error, calculated using the linear discriminant analysis after dimension reduction of two images Euclidean distance, the integration calculation of reconstruction error and the Euclidean distance difference between these two images level.
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Size: 220160 |
Author: 程德志 |
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Description: 中心显示格式求解poisson方程以及在平均值型误差下误差值，矩形区域，狄利克雷边界情况！-Center the display format for solving poisson equation in the average error under the error type, rectangular area, Dirichlet boundary conditions!
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Size: 1024 |
Author: luceayun |
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Description: 统计格点数据文件A和文件B中物理量间的相关系数，平均误差，均方根误差，绝对误差。-Statistics grid data file A and file B, the correlation between physical quantities, the average error, root mean square error, absolute error.
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Size: 1024 |
Author: 石晓冚 |
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Description: 这是模式识别中最小错误率Bayes分类器设计方案。
自行完善了在不同先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。
全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。
调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。
调用最小错误率贝叶斯分类器决策子函数时，根据先验概率数组，通过比较概率大小判断一个体重身高二维向量代表的人是男是女。
主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小错误率贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到不同先验概率条件下错误率的统计。
-This is the minimum error rate pattern recognition Bayes classifier design.
Self- improvement prior probability in different conditions , male , female and total error rate error rate statistics , into which each array .
All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions .
Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector third step is to application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function .
Call the minimum error rate decision Functions Bayesian
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Size: 4096 |
Author: 崔杉 |
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Description: 目标运动和卡尔曼跟踪的仿真程序，给出了系统状态转移矩阵和测量过程，以及协方差和增益。通过绘图得出仿真轨迹和真实轨迹的平均误差。有助于研究目标航迹跟踪-Target motion and Kalman tracking simulation program, the system state transition matrix and measurement process, as well as the covariance and gain. Obtained by drawing the average error of the simulation trajectory and real trajectory. Help researchers target trajectory tracking
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Size: 6144 |
Author: 周严 |
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