dsc2

1．研究窗函数的影响。 2．研究频率的物理分辨率和计算分辨率的区别。 (1. Study the impact of window function. 2. Study of the frequency resolution and computational resolution of the physical difference.)

在非理想情况下，在对经纬度实施更新的情况下，一直长度，求速度。(In the ideal case, the latitude and longitude of implementing the update situation, has length, and speed.)

dising fir filter in the fdatool matlab

matlab 数值积分工具箱 里面有具体说明。NIT(matlab numerical integration toolbox there are specific instructions. NIT)

用快速ICA算法实现信号分离，两进两出，调试通过。(fast ICA algorithm signal separation, the two-two, through debugging.)

地震波有限差分matlab模拟程序，输出可以为SGY格式，模拟地震波在地下介质中的传播(Seismic wave finite difference simulation program matlab, the output can be SGY format, simulate the propagation of seismic wave in underground medium )

CNM算法实现的快速社团检测，方便快捷好用(CNM Fast community detection based on the algorithm is convenient, fast and easy to use.)

conventional CRC coder for LTE-A

计算电力系统稳态潮流，使用直角坐标下的牛拉法，用牛顿迭代求解非线性方程组。计算结果包括节点电压、发电机注入功率、负荷注入功率支路始端功率、支路末端功率、支路损耗、网络损耗 (Calculation of the steady-state trend of power system, using ox-drawn in the rectangular coordinate method for solving nonlinear equations using Newton iteration. The calculation includes the node voltage of the generator into the power load to inject power slip began end power, slip the end of the power branch outages, network connectivity loss)

分析了支持向量回归机在能源需求预测中的优势,确定了输入向量集合和输出向量集合,建立了基于Matlab技术的SVR能源需求预测模型.对我国1985-2008年能源需求相关数据进行模拟与仿真,并对中国2010年和2020年能源需求量进行预测.研究结果表明:一是中国未来对能源的需求量逐渐增加,从2010年的330400万吨标准煤上升到2020年418320万吨标准煤,年均增长率为2.39％；二是在解决我国能源系统小样本.非线性及高维模式识别问题中SVR比BP神经网络等方法有更高的预测精度.(Support vector regression analyzes advantage in energy demand forecast to determine the set of input vectors and output vector set, established SVR energy demand forecasting model based on Matlab technology. Energy demand for our 1985-2008 related data modeling and simulation , and China in 2010 and 2020 energy demand forecast results show that: First, China s increasing demand for energy in the future, from 3.304 billion tons of standard coal in 2010 rose to 4.1832 billion tons of standard coal in 2020, average annual growth rate of 2.39 second is to solve our energy system small sample nonlinear and high dimensional pattern recognition problem SVR higher prediction accuracy than the BP neural network method.)