dctcode

说明：  matlab混合编程（书中程序） 第四章 (matlab mixed programming (program book) Chapter IV)

基于复数编码的遗传算法，是实数编码的扩展(Complex coding based on genetic algorithms, real coding is an extension)

支持向量机的研究现已成为机器学习领域中的研究热点，其理论基础是Vapnik[3]等提出的统计学习理论。统计学习理论采用结构风险最小化准则，在最小化样本点误差的同时，缩小模型泛化误差的上界，即最小化模型的结构风险，从而提高了模型的泛化能力，这一优点在小样本学习中更为突出。SVM理论正是在这一基础上发展而来的，经过十几年的研究和发展，已开始逐步应用于一些领域。在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势，已经在模式识别、函数逼近和概率密度估计等方面取得了良好的效果。( Support Vector Machine (SVM) is a new machine learning technique in recent years developed based on statistical learning theory (SLT). It wins popularity due to many attractive features and emphatically performance in the fields of nonlinear and high dimensional pattern recognition. The theory and algorithm of SVC is studied at first, then, simulation is to recognize handwritten numeral with the Lib-SVM toolbox. At last, we study the result, which shows that the SVC can do the classification problem with good performance, shorter operation time and is more suitable for real-time implementation.)

主要用于控制Boost电路的电压和电流，使用了神经网络，FGM，ADMM等控制算法(Mainly used to control the voltage and current Boost circuit using a neural network, FGM, ADMM and other control algorithm)

通过将喷泉码的编译码方法再无线衰落信道上的用matlab仿真出来(Multi-path ruili channel simulation research on matlab)

说明：  FIR滤波器设计，50Hz陷波器设计，带通滤波器设计(FIR filter design, 50Hz notch filter design)

GM11灰色预测模型，通过生成数据的gm(1,1)模型所得到的预测值的逆处理结果(Grey Forecasting)

先进PID控制及其MATLAB仿真，第二版的源码，对于学习控制仿真很有帮助(PID control)

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工程师和科学家们面临着用更少的时间建立复杂系统模型的需求，他们使用分布式和并 行计算来解决高性能计算的问题。这些分布式的环境由多处理器和多核计算机来实现。 Mathworks公司开发的分布式计算工具箱可以在多处理器计算环境中使用 MATLAB 和imulink 解决计算、数据密集型问题。可以使用该工具箱解决通过装配多个处理器包含几个单独工作单位或单个大型计算的问题。这些处理器可以驻留在一个多处理器计算机，或者，当工具箱配合 MATLAB 分布式计算引擎时，驻留在计算机集群上。(Engineers and scientists are facing with less time to build models of complex systems needs, they use distributed and parallel computing to solve the problem of high-performance computing. The distributed environment consists of multi-processor and multi-core computer to achieve. Mathworks Distributed Computing Toolbox, developed in multi-processor computing environment MATLAB and imulink solve computing, data intensive problems. You can use the toolkit to solve multiple processors by assembling several separate work units or containing a single large-scale computing problems. These processors can reside on a multi-processor computer, or, when the toolbox with MATLAB Distributed Computing Engine, it resides on a computer cluster.)