For2008a_2

资料很简单，但是是我自己初学做出来的，我只是希望能够在这个平台上多学习东西。谢谢(you can use it to study something babic.)

自动控制原理中应用仿真软件对系统的频率响应和稳定性的研究(The application of the principle of automatic control system simulation software, the frequency response and stability study)

模拟退化算法，用于解决TSP问题，有GUI(Analog degradation algorithm for solving TSP problem, there GUI)

DCSK QCSK CS-DCSK系统仿真 以及说明 可用 经过测试 (DCSK SYSTEM)

为了有效地应用遗传算法解决 鲁棒控制系统设计问题，将遗传算法与局部优化方法相结合，提出了基于降维扫描方法的自适应多目标遗传算法（DRSA-MOGA）。通过引入适应度函数标准化方法、基于最优Pareto解集搜索的降维扫描方法和适应度函数自适应调整方法，提高了算法的全局优化性能和局部搜索能力。仿真结果表明，DRSA-MOGA算法在不损失解的均匀度的情况下可以达到很高的逼近度(For effective application of genetic algorithms to solve robust control system design problems, genetic algorithms and local optimization method, based on reduced-order adaptive scanning method multi-objective genetic algorithm (DRSA-MOGA). Fitness function through the introduction of standardized methods, based on the Pareto optimal solution set of search methods and dimensionality reduction scan fitness function adaptive adjustment method, the algorithm improve the performance of global optimization and local search capabilities. The simulation results show that, DRSA-MOGA algorithm solution without loss of uniformity can be achieved under a high degree of approximation)

MATLAB project 1 images

parzen window density estimation with Gaussian as a smoothing factor

包含wcdma的上下行链路仿真，适用于无线通信仿真开发和学习(Contains wcdma uplink and downlink simulation, simulation development for wireless communication and learning)

：针对城市电网无功优化规划工作缺乏支撑工具的问题，结合实际业务特点，运用多层次的问题分解和 分层架构设计思想，研发了适用于城市高中压配网的无功优化规划软件模块，包括优化计算、原始数据与结 果数据管理子模块，涵盖现状年及目标年单阶段和多阶段无功优化规划功能。(Research on Reactive Power Planning Module for Urban Power Network)

说明：  用栅格模型表示工作环境,确定机器人运动起始结点和目标结点后,对工作环境进行分析,选取起始点与目标点之间连线附近的若干栅格,以被选取栅格为关键点,采用蚁群算法分别计算关键点与起始点和目标节点之间的最短路径,求取全局最短路径(With grid model to represent the working environment, robot motion after initial nodes and target, analysis of working conditions, selecting starting point and the target point near the connection between a number of grid, grid for the key points to be selected, key point and the starting point is obtained by using the ant colony algorithm respectively and the shortest path between the target node, calculate the global shortest path)