BP_net

改变声音信号采样频率。应分别考虑由低到高和由高到低，最好包括声音采集和回放等功能。(To change the audio signal sampling frequency. Should be considered separately from low to high and high to low, preferably including audio capture and playback functions.)

应用matlab语言，实现的蒙特卡洛算法，计算速度快(The Monte Carlo algorithm implemented in MATLAB language.)

BFGS算法: 可以解决无约束的最优化问题，如求目标函数的极值等。(BFGS algorithm: can solve unconstrained optimization problems, such as seeking the extreme, such as the objective function.)

粒子群算法，也称粒子群优化算法（Particle Swarm Optimization），缩写为 PSO， 是近年来发展起来的一种新的进化算法(（Evolu2tionary Algorithm - EA）。PSO 算法属于进化算法的一种，和遗传算法相似，它也是从随机解出发，通过迭代寻找最优解，它也是通过适应度来评价解的品质，但它比遗传算法规则更为简单，它没有遗传算法的“交叉”(Crossover) 和“变异”(Mutation) 操作，它通过追随当前搜索到的最优值来寻找全局最优。这种算法以其实现容易、精度高、收敛快等优点引起了学术界的重视，并且在解决实际问题中展示了其优越性。(Particle swarm optimization, also known as particle swarm optimization (Particle Swarm Optimization), abbreviated as PSO, is a new evolutionary algorithm developed in recent years ((Evolu2tionary Algorithm- EA) .PSO algorithm is an evolutionary algorithm, and similar genetic algorithm, it is also starting random solutions, through iterative search for the optimal solution, which is also uated by the quality of the fitness solution, but it is much simpler than genetic algorithms rule, it does not have the genetic algorithm cross (Crossover) and variant (Mutation) operation, which by following the current search to find the optimal value to the global optimum. the algorithm for its easy implementation, high accuracy, and fast convergence advantages attracted academic attention, and in solving practical problems demonstrated its superiority.)

Matlab Training Book

用于解决航天动力学，航天任务设计，导航制导控制问题(The Java Astrodynamics Toolkit is a library of components to help users create their own application programs to solve problems in astrodynamics, mission design, spacecraft navigation, guidance and control using Java or Matlab.)

FOR BETTER PROGRAMME MATLAB

numerical bisection and Newton methods by Deyvi

cooperative communication sources.

采用模糊C均值对数据集data聚为cluster_n类(Fuzzy C mean data on the data sets together to cluster_n class)