matlab

利用博弈论堆微电网规划，求出风光储能容量(Based on game theory, microgrid capacity planning is realized)

inventory sales system

In this study, it is tried to employ a state-of-the-art multi-objective uniform-diversity genetic algorithm (MUGA) for Pareto optimization of PI/PID controllers in Load Frequency Control (LFC) of power systems. At fi rst, multi objective optimization of a linear non-reheat two-area interconnected power system is conducted with respect to three confl icting objective functions. Gains of PI and PID controllers are con- sidered as design variables while the objective functions are Integral Time multiply Absolute Error (ITAE),

dSPACE培训教程入门，控制系统半实物仿真的最好工具学习资料(dSPACE training module entry, the control system of the best semi-physical simulation tool for learning materials)

官方提供的唯一的HL7 V3的API例子(The only API example of the official HL7 V3 provided by the official)

STM8L15系列的I2C通信实例程序,有三种模式的范例,分别是master,slaver ,poll(STM8L15 series I2C communication procedures example, there are three modes of examples, namely master, slaver, poll)

用Winamp制作的m3u播放列表转换成通用格式

说明：  一个很经典的GPS短基线解算程序，matlab编写(A classical GPS baseline processing software, developed using Matlab)

说明：  calculate wear the subroutine coded by Fortran and can be used for the 2D model and useful for the wear calculation

说明：  模拟退火是 80 年代初发展起来的一种随机性组合优化方法。它模拟高温金属降温的热力学过程，并广泛应用于组合优化问题。基于模拟退火的粒子群优化算法是把模拟退火机制引入基本粒子群优化算法中，采用杂交粒子群优化算法中的杂交运算和带高斯变异的粒子群优化算法中的变异运算，以便进一步调整优化群体。(Simulated annealing is a stochastic combinatorial optimization method developed in the early 1980s. It simulates the thermodynamic process of high temperature metal cooling and is widely used in combinatorial optimization problems. The particle swarm optimization algorithm based on simulated annealing introduces the simulated annealing mechanism into the basic particle swarm optimization algorithm, and adopts the hybrid operation in hybrid particle swarm optimization algorithm and the mutation operation in particle swarm optimization algorithm with Gaussian mutation, so as to further adjust the optimization population.)