interpolationimage

MATLAB从入门到精通 书上源代码，人民邮电出版社(MATLAB books from the entry to the master source code, Posts & Telecom Press)

基于内容的图像检索算法的研究，颜色直方图算法。(Content-Based Image Retrieval Algorithm，Color histogram algorithm)

This is a simple example in Matlab where a array is introduced

analisys for image(RGB) on matlab,using matlab grafic user

本程序提供了在空间相关信道下，差分空时码的源代码(This procedure provides a channel in the space-related, differential space-time source code)

交流位置伺服系统因其存在参数时变、负载扰动以及电机的非线性等缺点， 很难为其建立准确的模型. 模糊（ fuzzy）控制具有无需建立被控对象的数学模型、鲁棒性好等 优点，但稳态精度差. 将模糊控制和PID 控制相结合，设计了Fuzzy-PID 双模控制器. 在论域内 用不同的控制方式分段实现控制，这样就综合了模糊控制和PID 控制的优点，克服了各自的缺 点. 该控制器结构简单、易于实现. 在半闭环交流位置伺服系统上所做的实验表明，采用Fuzzy- PID 双模控制器与采用经典PID 控制器相比，阶跃响应的调整时间减少了33. 7 ，并且无超 调，轨迹跟踪误差减小了47. 2 .(AC servo system because of the presence of parameter variations, load disturbances and non-linear motors and other shortcomings, it is difficult to establish accurate models for fuzzy (fuzzy) control without having to build a mathematical model of controlled object, robustness and good However, steady-state accuracy is poor. fuzzy control and PID control are combined to design a dual-mode Fuzzy-PID controller in the sub-region to achieve control on the different control methods, so that a combination of fuzzy control and PID control advantages to overcome their disadvantages. simply the controller structure, easy to implement. experiments on semi-closed loop AC servo system did show that the Fuzzy-PID controller is compared with the classic dual-mode PID controller, step response The adjustment time is reduced by 33.7 , and no overshoot, tracking error is reduced 47.2 .)

核主元分析和支持向量机结合的故障诊断方法(KPCA and SVM fault diagnosis method combining)

针对五单元圆型天线阵干涉仪测向系统，分别对等基线和不等基线的结构和相位模糊进行了分析；通过对多基线测量数据的加权处理提高测向精度，提出了权系数的选择方法和测向信号处理算法，其中包括相位差估计、角度变换、解模糊和接收通道误差校正等；计算机仿真结果表明两种结构基线的测向精度非常接近，在全方位、超短波频段内的最大测向误差小于0.10。(eee)

关于量子和量子群优化算法求解NP问题的论文，其中还包括一篇介绍详细的硕士论文，全中文，对于初学者很有帮助。(On the Quantum and Quantum Swarm Optimization for NP issues papers, including a detailed introduction of the master)

自举是一种由重采样估计，独立和（蒙特卡洛重采样）等概率设置一个单一的数据统计变化的一个途径。允许的措施估计那里的潜在分布是未知的或者样本量很小。他们的结果与这些分析方法的统计特性相一致。 在这里，我们使用非参数逼近。非参数引导更简单。它不使用该模型的结构，建造人工数据。矢量[易西]是重采样，而不是直接与replecement。这些参数是从这些对构建。 二，回归模型时，应使用在回归方程中的两个变量是随机的，会有错误的，即不是由研究者控制。模式，我用普通最小二乘回归低估了变量之间的错误时，他们都含有线性关系的斜率。据索卡尔和罗尔夫（1995），模型二回归的主题是一对哪些研究和争论仍在继续，最终建议是很难做出。 BOOTGMREGRESS模型II是一个引导程序。这需要s引导和规范样品前坡计算变量。这两个变量的每个转化为具有零均值和标准差的一个。由此产生的斜率是线性回归系数的Y在X里克创造了这个词（1973）几何平均数并给出了一个模型II回归广泛审查。它也被称为引导标准的主要轴线(The bootstrap is a way of estimating the variability of a statistic from a single data set by resampling it independently and with equal probabilities (Monte Carlo resampling). Allows the estimation of measures where the underlying distribution is unknown or where sample sizes are small. Their results are consistent with the statistical properties of those analytical methods. Here, we use the Non-parametric Bootstrap. Non-parametric bootstrap is simpler. It does not use the structure of the model to construct artificial data. The vector [yi, xi] is instead directly resampled with replecement. The parameters are constructed from these pairs. Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear relationship between the variables when they both contain error. According to Sokal and Rohlf (1995), t)