xinxingxian

说明：  电动汽车控制策略研究 天津大学电气与自动化工程学院 通过MATLAB/SIMULINK仿真软件对各种控制方案和再生制动策略 分别进行了仿真研究和对比，得出各种控制策略能够提高电动汽车性能的结论。 同时将仿真结果与实验室和台架实验系统的实验结果做了相应的研究与比较，进 一步验证了上述控制策略的可行性。 (Study on Control Strategy for Electric Vehicle )

c++通用技能编码能力提升：组件安装管理程序，已测试。(c++ generic skills coding capacity building: Component Installation Manager has been tested.)

稀布阵列matlab波束形成程序，可画出9阵元稀疏阵列的阵列方向图和阵列布阵示意图，有详细的注释，适合初学者参考(thinned array beamforming)

Localization problem(A matlab code for sensor localization)

为那些没有电机却想调试PID电机驱动的人能够熟悉PID算法而做的simulink模型和边界法整定PID参数的讲解(Want to debug, but for those who do not have the motor PID motor drive people to become familiar with the PID algorithm to do simulink model and boundary method tuning PID parameters to explain)

福大MATLAB课件 第一章MATLAB6.5 环境 􀂋 第二章MATLAB数值计算 􀂋 第三章MATLAB符号计算 􀂋 第四章MATLAB的可视化输出和GUI设计 􀂋 第五章MATLAB程序设计 􀂋 第六章线性控制系统分析和设计 􀂋 第七章Simulink仿真环境(Tai Fook MATLAB environment courseware Chapter MATLAB6.5 )

说明：  cma自适应恒模算法，由接受信号估计信道的自适应恒模算法(cma constant modulus adaptive algorithm, to receive signals from the adaptive channel estimate constant modulus algorithm)

PN sequence generation using matlab

介绍matlab原理和功能，并包含了matlab最基本的函数学习(Describes the principle and function of matlab, and contains the most basic function of learning matlab)

优秀论文及配套源码。首先阐述了负荷预测的应用研究现状，概括了负荷预测的特点及其影响因素，归纳了短期负荷预测的常用方法，并分析了各种方法的优劣；接着介绍了作为支持向量机（SVM）理论基础的统计学习理论和SVM的原理，推导了SVM回归模型；本文采用最小二乘支持向量机（LSSVM）模型，根据浙江台州某地区的历史负荷数据和气象数据，分析影响预测的各种因素，总结了负荷变化的规律性，对历史负荷数据中的“异常数据”进行修正，对负荷预测中要考虑的相关因素进行了归一化处理。LSSVM中的两个参数对模型有很大影响，而目前依然是基于经验的办法解决。对此，本文采用粒子群优化算法对模型参数进行寻优，以测试集误差作为判决依据，实现模型参数的优化选择，使得预测精度有所提高。实际算例表明，本文的预测方法收敛性好、有较高的预测精度和较快的训练速度。(first expounds the recent application research of load forecasting, summarized the characteristics of load forecasting and influencing factors, summed up common methods of short-term load forecasting, and analyzed the advantages and disadvantages of each method then introduced statistical learning theory and the principle of SVM as the basis of support vector machine (SVM ) theory, SVM regression model is derived this paper adopted least squares support vector machine (LSSVM) model, according to the historical load data and meteorological data of a certain area of Zhejiang Taizhou, Analysised the various factors affecting the forecast, summed up the regularity of load change , amended "outliers" in the historical load data,the load forecasting factors to be considered were normalized. The two parameters of LSSVM have a significant impact on the model, but it is still soluted based on the experience currently. So, this paper adopted particle swarm optimization algorithm to optimized )