插值和拟合程序

说明：  信号时序幅频仿真图(timing signal amplitude and frequency simulation map)

此程序代码是实现将十进制小数转换为二进制数。(Achieve decimal is converted to a binary number)

倒立摆的模糊控制实现的是其位移速度角度角速度的控制(Inverted pendulum fuzzy control is the control of the speed of the displacement angle angular velocity)

this file is a simulation of simple boost control in z_zource inverter

ofdm 环路延迟估计，包括整数倍估计和小数倍估计。估计误差可调。(The ofdm loop delay estimation, including an integer multiple of estimates and a small multiple of the estimated. The estimation error is adjustable.)

利用Kronecker的模型产生瑞利衰落信道 很好。(Kronecker model produced using Rayleigh fading channels good.)

matlab学习教程 讲学习的基本内容，对新手很有帮助 英文讲解，但很容易听得懂(matlab tutorial stresses learning to learn the basic content helpful for the novice English to explain, but easy to understand)

说明：  多目标遗传算法是NSGA-II[1]（改进的非支配排序算法）,该遗传算法相比于其它的多目标遗传算法有如下优点：传统的非支配排序算法的复杂度为 ，而NSGA-II的复杂度为 ，其中M为目标函数的个数，N为种群中的个体数。引进精英策略，保证某些优良的种群个体在进化过程中不会被丢弃，从而提高了优化结果的精度。采用拥挤度和拥挤度比较算子，不但克服了NSGA中需要人为指定共享参数的缺陷，而且将其作为种群中个体间的比较标准，使得准Pareto域中的个体能均匀地扩展到整个Pareto域，保证了种群的多样性。（消除了共享参数）。(Multi-objective genetic algorithm is nsga-ii [1] (improved non-dominant sorting algorithm), which has the following advantages compared with other multi-objective genetic algorithms: the complexity of the traditional non-dominant sorting algorithm is, while the complexity of nsga-ii is, where M is the number of objective functions and N is the number of individuals in the population.The introduction of elite strategy to ensure that some good individuals in the evolutionary process will not be discarded, thus improving the accuracy of the optimization results.The comparison operator of crowding degree and crowding degree not only overcomes the defect that NSGA needs to specify the Shared parameter artificially, but also takes it as the comparison standard between individuals in the population, so that individuals in the quasi-pareto domain can uniformly expand to the whole Pareto domain, ensuring the diversity of the population.(eliminating Shared parameters).)

TSP can be defined as a problem where starting from a node it is required to visit every other node only once in a way that the total distance covered is minimized. After an initial solution a neighborhood solution is selected by an exchange of a randomly selected pair of nodes. The randomly generated neighbor solution is selected if it improves the solution else it is selected with a probability that depends on the extent to which it deteriorates from the current solution.

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