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SimEvents® extends Simulink® with tools for discrete-event simulation of the transactions between components in a system architecture. You can use the architecture model to analyze performance characteristics such as end-to-end latencies, throughput, and packet loss. SimEvents can also be used to simulate a process, such as a mission plan or a manufacturing process, to determine resource requirements or identify bottlenecks.

THIS DOCUMENT IS ABOUT OF PROJETCS IN MATLAB

说明：  本书详细介绍了偏微分方程的MATLAB解法，是解决偏微分方程的一种有效的数值解法。(The book details the MATLAB Solution partial differential equations, partial differential equations to solve an effective numerical method.)

说明：  分析了现有船舶自动避碰系统的不足,提出了基于人工势场法的船舶自动避碰系统. 根据 船舶的操纵特性,建立了引力场和斥力场函数,实现船舶自动避碰航行的合力和方向. 在电子海图 所建立的仿真环境中,验证了人工势场法可用于船舶自动避碰系统中.(Analysis of existing ships lack of automatic collision avoidance system is proposed based on artificial potential field method automatic collision avoidance system for ships. According to the ship maneuvering characteristics, the establishment of a gravitational field and the repulsive force field function, automatic collision avoidance to achieve ships sailing together, and direction. in the electronic chart of the simulation environment created to verify the artificial potential field method can be used to ship automatic collision avoidance system.)

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matlab版SIFT算法演示程序，简洁易懂易用，实际算法部分用C来实现(SIFT algorithm demo program of matlab version)

CVPARTITION Create a cross-validation partition for data. An object of the CVPARTITION class defines a random partition on a set of data of a specified size. This partition can be used to define test and training sets for validating a statistical model using cross-validation.

遗传算法在线优化PID参数，能够实时控制对象(Genetic Algorithm optimization PID parameters online, real-time control object)

样本熵,用于进行信号的复杂度计算，已调试完成，可直接调用(sample entropy can be used to analyze the complexity of signal.)

例1 采用动量梯度下降算法训练 BP 网络。 例2 采用贝叶斯正则化算法提高 BP 网络的推广能力。在本例中，我们采用两种训练方法，即 L-M 优化算法（trainlm）和贝叶斯正则化算法（trainbr），用以训练 BP 网络，使其能够拟合某一附加有白噪声的正弦样本数据。(Example 1 uses the momentum gradient descent algorithm to train the BP network. Example 2 uses the Bayesian regularization algorithm to improve the generalization ability of BP network. In this example, we use two training methods, the LM optimization algorithm (trainlm) and the Bayesian regularization algorithm (trainbr), to train the BP network to fit a sine sample with white noise data. )