mmread

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):

Resource allocation in LTE matlab code

Off-line signature verification based on grey level information using texture features

这是一个关于潮流计算的Matlab小程序，可以帮助初学者熟悉电力系统方面相关程序编写(This is a small program on Matlab flow calculation, can help beginners familiar with aspects of the power system-related programming)

内容包括：插值与拟合实验，代数方程组的解法，微分方程实验，优化问题实验，综合实验(Include: interpolation and fitting the experimental, the solution of algebraic equations, differential equations experiment, optimization experiments, comprehensive experimental)

说明：  井中地震正演程序，matlab程序编写(Borehole seismic forward procedure, matlab programming)

CLSPSO：用混沌粒子群优化算法求解无约束优化问题 (CLSPSO: Chaos particle swarm optimization algorithm for solving unconstrained optimization problems)

雷达定位精度分析，绘制GDOP图，研究雷达布阵形式！(Radar positioning accuracy analysis, drawing GDOP maps, radar lineup in the form of research!)

模拟数字基带系统在matlab下的实现，包括输入输出信号的码型，功率谱密度，眼图及误码率(Analog-to-digital base-band system in the realization of matlab, including the code input and output signal type, the power spectral density, eye diagram and bit error rate)

The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order (The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order)