uipickfiles

Robotics TOOLBOX The Toolbox provides many functions that are useful in robotics including such things as kinematics, dynamics, and trajectory generation. The Toolbox is useful for simulation as well as analyzing results from experiments with real robots. Have fun with the toolbox. It is self-explanatory and very (robotics) user friendly...see the robot.pdf

说明：  主要包括了用gui设计的计算机界面，能通过控件进行计算(including the use gui computer interface design, control through calculated)

经典算法 虽然小，但是经常用到。尤其是有的时候面试(Classical algorithm may be small, but often used. Sometimes the interview, especially)

这是拿matlab编写的jpeg编码的程序，内含报告，对于北邮选多媒体选修的很有用(This is written in matlab to take jpeg encoding process, containing reports, selected for Beijing University of Posts and Telecommunications of useful multimedia elective)

非常实用的基2DIT-FFT函数，直接运行可以分析原理。(Very practical base 2DIT-FFT function, operation can be analyzed directly principle.)

BER for BPSK modulation in 3 tap ISI channel

BACKSAMP Backsampling the subband images of the directional filter bank y = backsamp(y) Input and output are cell vector of dyadic length This function is called at the end of the DFBDEC to obtain subband images with overall sampling as diagonal matrices See also: DFBDEC

永磁同步电机的无传感控制策略，包括模型参考自适应算法、滑模观测器算法等(Sensorless control strategy of PMSM, including model reference adaptive algorithm, sliding mode observer algorithm, etc.)

小波变换的MATLAB变换程序 可以对信号进行降噪声处理(xiaobobianhuan.m)

优秀论文及配套源码。首先阐述了负荷预测的应用研究现状，概括了负荷预测的特点及其影响因素，归纳了短期负荷预测的常用方法，并分析了各种方法的优劣；接着介绍了作为支持向量机（SVM）理论基础的统计学习理论和SVM的原理，推导了SVM回归模型；本文采用最小二乘支持向量机（LSSVM）模型，根据浙江台州某地区的历史负荷数据和气象数据，分析影响预测的各种因素，总结了负荷变化的规律性，对历史负荷数据中的“异常数据”进行修正，对负荷预测中要考虑的相关因素进行了归一化处理。LSSVM中的两个参数对模型有很大影响，而目前依然是基于经验的办法解决。对此，本文采用粒子群优化算法对模型参数进行寻优，以测试集误差作为判决依据，实现模型参数的优化选择，使得预测精度有所提高。实际算例表明，本文的预测方法收敛性好、有较高的预测精度和较快的训练速度。(first expounds the recent application research of load forecasting, summarized the characteristics of load forecasting and influencing factors, summed up common methods of short-term load forecasting, and analyzed the advantages and disadvantages of each method then introduced statistical learning theory and the principle of SVM as the basis of support vector machine (SVM ) theory, SVM regression model is derived this paper adopted least squares support vector machine (LSSVM) model, according to the historical load data and meteorological data of a certain area of Zhejiang Taizhou, Analysised the various factors affecting the forecast, summed up the regularity of load change , amended "outliers" in the historical load data,the load forecasting factors to be considered were normalized. The two parameters of LSSVM have a significant impact on the model, but it is still soluted based on the experience currently. So, this paper adopted particle swarm optimization algorithm to optimized )