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MATLAB开发工具用书。全面反映MATLAB通用功能，强调理论概念，教材算法和实际计算三者关系。(MATLAB development tools, books. Fully reflect the general-purpose MATLAB functions, emphasizing theoretical concepts, materials, algorithms, and the relationship among the actual calculations.)

粒子群搜索的matlab程序，对于一个隐函数寻优，四维隐含数嵌入在子函数中，可根据需要更改，(PSO Matlab search procedures, an implicit function optimization, 4D implied several embedded in the Functions, may need to change.)

介绍了什么是函数文件及如何编写函数文件等等(What is the function of the document describes how to write functions and files, etc.)

常微分方程组的两种解法--打靶法和配置法(two numerical methods of odes)

多用户MIMO系统的一种干扰消除类预编码迫零预编码（ZFBF）(an interference elimination algorithm for multi-user MIMO system-Zero Forcing precoding(ZFBF) )

电力负荷预测是电力系统规划的重要组成部分，也是电力系统经济运行的基础，其对电力系统规划和运行都极其重要。利用粒子群算法优化支持向量机更加高效准确预测电力负荷。(The power load forecasting is an important part of the power system planning, and it is also the basis of the economic operation of the power system. It is very important to the planning and operation of the power system. Particle swarm optimization is used to optimize SVM to predict power load more efficiently and accurately.)

this code to generate parity check matrix of Low density parity check cod, and encode in order to codeword

单边带调制信号产生和解调的仿真，实现单边带调制解决调制中遇到的问题(Single sideband modulation signal generation and demodulation simulation, to achieve single sideband modulation problem encountered in the modulator)

说明：  视频帧序列实现基于GMM（高斯混合模型）的背景建模(Video frame sequence implementation based on GMM (Gauss mixture model) background modeling)

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)