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matlab中神经网络的介绍 很好的资源 大家的指导书(matlab neural network in the U.S. introduce a very good resource guide book)

如何在Simulink中改变其状态量的顺序的实例程序，主要用于控制系统的设计(Explores how to leverage the Structure with Time Simulink data format to alleviate ordering and index issues on inputs/outputs/states.)

基于MFC编写的界面，用VC与matlab混编实现马拉特小波分解，有文档注释，适合初学者( MFC-based interface prepared by VC and matlab mixed to achieve Marat wavelet decomposition, documentation notes, suitable for beginners)

说明：  此图是hamming仿真等几个具体小仿真实验(This map is hamming simulation, and several specific small simulation experiment)

此程序在matlab环境下运行，是完整的tdpsola和fdpsola程序，包含了前期的语音读入，预处理，清浊音判决等等很全面(This procedure to run in matlab environment, is a complete program tdpsola and fdpsola include pre-read the speech, pre-processing, audio Qingzhuo comprehensive judgments, etc.)

关于反应谱精确法的求解，是大家经常做反应谱分析用到的(About response spectrum method of solving the accurate, we are often do response spectrum analysis of use )

MATLAB Simulink 通信系统 建模 仿真实例 精讲 源代码(MATLAB Simulink communications system modeling and simulation examples the Jingjiang source code)

一个会动的五角星,应用matlab软件编程。(A moving star，using matlab program.)

此程序用来产生不同n条件下的固有频率并画出振型图(The program used to generate the N under different natural frequency and vibration mode diagram drawn )

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)