SVR

matlab7.0中工具箱em中的部分源程序(matlab7.0脰脨 鹿 陇 戮 脽脧盲em脰脨渭脛 虏 驴 脰脭 )

说明：  这是一些例程，包括了汇编、m文件，如对站有帮助，则无比荣幸 (This is some routines, including a compilation, m documents, such as the right, there is help, incomparable honor)

用matlab 语言实现功率谱估计算法,程序中有详细的说明(using Matlab language power spectrum estimation algorithm, the process is described in detail in)

The model used for creating the reference voltage is shown in Fig. 4. First, photovoltaic output current (Ipv) and output voltage (Vpv) are passed through a first order low pass filter with a magnitude of G = 1 and a time constant of T = 0.01 seconds in order to filter out the high frequency components or harmonics from these signals as shown in Fig. 5 and Fig. 6. The filtered current and voltage signals (Ipv_F and Vpv_F) are then fed into the MPPT control block that uses the Incremental Conductance Tracking Algorithm. An algorithm that is based on the fact the slope of the PV array power curve shown in Fig. 7 is zero at the Maximum Power Point (MPP), positive on the left of the MPP, and negative on the right. The MPP can thus be tracked by comparing the instantaneous conductance (I/V) to the incremental conductance (∆ I/∆ V) [11] as in (1):

超导磁储能系统抑制风力发电功率波动的研究(Research on the applica tion of superconducting magnetic energy storage un it to damp wind genera tion power fluctua ting)

最大相关熵准则自适应滤波，凸组合，两个滤波器(mcc filtering, convex combination, two filters)

说明：  一个高阶系统转换为状态空间的matlab算法，附有原理(A high-end system into a state space matlab algorithm, with a principle)

转速电流双闭环直流调速系统的仿真文件，仿真结果相当满意(The Simulation of Double Closed-Loop DC Speed Control System )

this is a sorce code matlab for interpolation data and create 2d graph

用Parzen窗法或者kn近邻法估计概率密度函数，得出贝叶斯分类器，对测试样本进行测试，比较与参数估计基础上得到的分类器和分类性能的差别.2． 同时采用身高和体重数据作为特征，用Fisher线性判别方法求分类器，将该分类器应用到训练和测试样本，考察训练和测试错误情况。将训练样本和求得的决策边界画到图上，同时把以往用Bayes方法求得的分类器也画到图上，比较结果的异同。3．选择上述或以前实验的任意一种方法，用留一法在训练集上估计错误率，与在测试集上得到的错误率进行比较。(Use Parzen Windows method or the kn neighbor method estimated probability density function, draw the bayes classifier, to test sample to test, and the parameter estimation is based on the classifier and get the difference of classification performance. 2. At the same time the height and weight data as the characteristic, with Fisher identification method for linear classifier, and the application of the classifier to training and test sample, investigation and test training error condition. Will the training sample and utility of decision on the drawing boundary paint, and at the same time before the Bayes method with optimal classifier also paint chart, the result of the comparison of the similarities and differences. 3. Select the above or before any a kind of method of experiment, with a method for training set in the estimation error rates, and on the test set get error rates are compared.)