资源列表
50th-Filtering
- 基于最少方差算法的电流50次谐波的Matlab的M文件程序，用于谐波分析-Current is 50 times harmonic based on minimum variance algorithm Matlab M file, used for harmonic analysis
fivenode
- 基于matlab的电力系统五节点的潮流计算和分析。得到了潮流计算个节点的数据信息。-Based on five-node power system matlab flow calculation and analysis. Data flow calculation nodes.
smalli
- 基于matlab的小电流接地系统单相故障分析，得到了系统对敌电压和线电压的波形图。-Matlab small current grounding system single-phase fault-based analysis, waveform diagram of system voltage and line voltage the enemy.
STATCOM_Base_on_PIcontrol
- 基于PI控制的电力系统静态无功补偿器Simulink仿真，用于验证控制算法的有效性-STATCOM_Base_on_PIcontrol used to verify the effectiveness of the control algorithm It is achieved by Matlab/Simulink.
ellipsoid_fit
- Ellipsoid fit, fit ellipse on 3d data, matlab
CancerCellPanINDataBinned
- we developed a toolkit called PriVar, a systematic prioritization pipeline that takes into consideration calling quality of the variants, their predicted functional impact, known connection of the gene to the disease and the number of mutations
ICA
- Independent Component Analysis Play a Role in Unmixing Hyperspectral Data-This paper studies the impact of hyperspectral source statistical dependence on ICA and IFA performances
UDRRS
- This a compact code for reliability analysis based on the stochastic response surface method (SRSM) which first uses the univariate dimension reduction (UDR) to decompose the multidimensional performance function into multiple one-dimensional univari
Digital_PID_Chap1
- 数字PID算法的Mtalab仿真例程，包含M文件程序和Simulink仿真-Digital PID algorithm of Mtalab simulation routines, including M file and Simulink simulation program
APF_ip_iq
- 基于ip/iq变换的电力系统APF仿真，用于验证控制算法的性能和进行参数选取的辨识-Power system simulation of APF based on IP/IQ transform, is used to verify the performance of the control algorithm and parameters selection of identification
ELM
- In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed.-The experimental results based on a few artificial and real benchmark function approximation and classification problems including very l
ELMsupplement
- 对极限学习机的数据的补充。This paper has demonstrated that ELM can be used efficiently in many applications, however, two more interesting aspects are still open: the universal approximation capability9 of ELM and the performance of ELM in sparse high-dim