NSGA2 eva

Convolutional(2,1,6) Encoder and soft decision Viterbi Decoder

阵列天线抗干扰 圆阵等高线，波束方向及三位角度信噪图(Array antenna interference contours, angle of beam direction and three noise graph)

eye diagram plot matlab code

对不同传感器得到的图像进行图像融合，已得到更有利于人或计算机视觉的图像。更好的满足人们的需求(iamge fusion )

RD 算法 实现SAR成像 （需要雷达实测数据）(SAR imaging algorithm RD (measured radar data needed))

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).)

用复化梯形公式来求解积分的一个应用，用matlab和c++写的(With rehabilitation of trapezoidal integral formula for solving an application, using matlab and c++ Written)

说明：  粒子滤波程序,对目标进行跟踪，附有状态方程和量测方程(PF)

层次分析 w0为准则层权重，w1为方案层权重， ts为总权重(Analytic Hierarchy guidelines layer weights, w1 program layer weights, ts total weights w0)

用c语言和matlab联合编程求解非线性薛定谔程序，应用CUDA并行技术(nonlinear schrodinger equation solver)