Adaptive-probabilities-of-crossover

说明：  Cooperative Communications in Ultra-Wideband Wireless Body Area Networks: Channel Modeling and System Diversity Analysis

matlab写的神经网络的几个演示程序，有的是自己写的，有的是根据别人写的改的，bp、rbf、pnn、hopfield等(matlab neural network to write a few demo program, some wrote it myself, some other people to write in accordance with the change, bp, rbf, pnn, hopfield etc.)

基于matlab系统辨识的经典脉冲响应辨识，自己编写，效果不错。(Matlab system identification based on the classic impulse response identification, their preparation, good results.)

modelong and simulation in matlabb

燃气轮机matlab模型,相信会对研究微网的同学有帮助。(matlab generator)

simlingk仿真与编程，如何使用simlingk，以及一些工程案例(simlingk simulation and programming, how to use simlingk, and some Projects)

提出了一个混沌系统,并对该系统的基本动力学特性进行了深入研究.得到该系统的Lyapunov指数、Lyapunov 维数,给出了Poincaré映射图以及时域图和相图.运用电子工作平台EWB软件对实现该新混沌系统的振荡器电路进行了仿真实验.经过数值仿真和电路系统仿真证实该系统与以往发现的混沌吸引子并不拓扑等价,属于新的混沌系统.(Presents a chaotic system, and the basic dynamic characteristics of the system were studied. Get Lyapunov exponent of the system, Lyapunov dimension, given the Poincar maps and graphs and time-domain phase diagram using electronic work platform EWB software for implementing the new chaotic system oscillator circuit simulation experiments through numerical simulation and circuit and system simulation confirmed that the system ever discovered chaotic attractor is not topologically equivalent, is the new chaotic system.)

本算法能够按照两种策略生成并绘制随机图，但是对于生成的随机图的属性分析不够完善，从代码中能够看出作者并没有完成本算法关于“平均路径长度”、“聚类系数”和“平均度”三个属性的求解。(The algorithm is able to follow two strategies to generate and draw random graph, but for the properties generated random graph analysis is not perfect, it can be seen from the code of the algorithm is not complete on the "average path length", "clustering coefficient" and "the average degree of" solving the three properties)

matlab仿真模型，可以当作毕业设计内容使用。32变换模型(matlab simulation model can be used as a graduation project content. 32 transformation model)

In this project, the standard particles swarm optimization - named here (MyPSO) is simulated and tested by five well-known benchmark functions (Sphere, Ackley, Rastrigin, Rosenbrock and Schwefel p2.26). Then, a new mechanism- named here (Improved MyPSO) is introduced to improve the performance of the standard particles swarm optimization to achieve better results and avoid stagnation at bad results. This new mechanism is also tested by the same benchmark functions. The results of improved PSO are then compared with those of standard PSO and different published improved PSO (SPSO, PSO-XD, CPSO-S and PSO-P5).