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说明：  matlab电机仿真精华50例\异步电机dq0仿真模型(matlab simulation of the essence of 50 cases of electric induction motor simulation model dq0)

递归极大似然数估计，在实际通信系统仿真过程中非常有用(Recursion number of maximum likelihood estimate, in actual communication system simulation process is very useful )

改进的有序统计类恒虚警检测器（MOS-CFAR），适合于多目标检测(detectors based on the modified ordered statistic constant false alarm rate (MOS-CFAR) technique.)

本源代码给出了一个完整的蜂窝移动通信系统的仿真程序实例,开发环境是matlab,希望可以从事此方面工作的人员提供一个借鉴.(Source code is given a complete cellular mobile communication system simulation program examples, the development environment is matlab, hope that we can engage in this work provide a reference.)

说明：  雷达接收机的重要部件，包含AD、混频器、高放和中放4部分。(Important parts of radar receivers, including AD, mixers, high level and intermediate level radioactive Part 4.)

APF SVG matlab souce

对HARQ系统的吞吐量分析，isodata 迭代自组织的数据分析，可实现对二维数据的聚类。( HARQ throughput analysis of the system, Isodata iterative self-organizing data analysis, Can realize the two-dimensional data clustering.)

這是一個Matlab的麻將圖紋辨識的源碼,內部有附上相關註解(This is a pattern recognition Mahjong Matlab source code, internal comments have attached the relevant)

判断一个数是否为素数。可以作为分析研究素数编程的人参考。(To determine whether a number is prime. Analysis of prime numbers can be used as programming easy reference)

(1)针对在线计算量大这一缺陷，将预测控制中的柔化输出信号的思想推广到柔化输入信号，使得约束条件被简化为仅对当前控制量的约束，可以直接计算得出；同时该方法避免了求逆矩阵，大大减小了计算量，并能够保证控制算法的可行性和良好的控制性能。 (2)针对传统算法中设计参数整定困难这一缺点，应用基于BP神经网络变参数设计的广义预测控制算法，实现了对控制量柔化参数的在线调整。 (3)利用带有遗忘因子的最小二乘法对系统辨识。本文通过仿真发现该方法对于Hénon混沌系统并不完全适用，可考虑利用其他优化系统辨识的方法对本方法进行改进，以期达到更好的辨识效果。 (4)针对系统稳定性分析复杂，本文在控制增量前加入前馈因子，保证所选的Lyapunov函数使闭环系统满足Lyapunov稳定判据，由此证明闭环系统稳定。 (1. To solve the problem of GPC huge computation, algorithm with input increment constraints is presented in which the concept of output softness was used to soften the input increments．As a result, the constraints are simplified to be the only one constraint on the current control increment which can be computed directly. At the same time, it needn’t computing the inverse matrix and thus reduces large computation. Moreover, it guarantees the feasibility of the algorithm and has good control performance． 2. To overcome the difficulty in the choice of tuning parameters in traditional GPC, a GPC algorithm with variable parameter design based on BP neural network. is presented，in which the input softness parameters are tuned on line. 3. In this paper, we Identify system by using the least square method with forgetting factor. However, after system simulation, we realize that this method doesn’t fit the Hénon chaotic system perfectly. So we recommend modify this method by other Optimizati)