MPPT with Fuzzy

自己写的小程序，包括了DAB系统各部分的波形及功率谱图(Write their own small procedures, including the DAB system, the various parts of the waveform and power spectrum)

说明：  Funcion, that is ussually used for loading Bin-files in Matlab

ParInfoGain - Computes parallel information gain and gain ratio in Matlab using the Matlab Parallel Computing Toolbox or the Distributed Server (if available) Information gain is defined as: InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute) Gain ratio is defined as: GainRatio(Class, Attribute) = (H(Class) - H(Class | Attribute)) / H(Attribute). ...where H is the entropy, defined as - sum(i=1 to k) pi log2 pi

FIR High pass filter simulation using matlab

自写的基于MUSIC的ISM宽带测角谱估计算法，线性调频信号，4阵元，2目标，正常运行。(My own MUSIC based ISM broadband DOA estimation algorithm. Chirp signals, four elements, two objection. Functioning properly.)

使用matlab语言实现现代信号处理_谱估计_周期图法(Modern Signal Processing PSD Period Graph Method.)

本程序用matlab仿真了广义预测控制的显式解法。(This procedure matlab simulation using generalized predictive control method explicit.)

说明：   这是一个完整的指纹识别程序，它包括了直方图均衡，Gabor滤波图像增强，方向图过滤，纹理细化，特征提取及特征匹配。其中，特征匹配包含了3种匹配方法，另外还附有PPT，非常值得研究。(Finger Identification)

说明：  多目标货物配装问题是一个复杂的组合优化问题，属于NP难问题，本文用混合粒子群算法求解多目标货物配装问题。混合粒子群算法在基本粒子群算法的基础上，通过引进遗传算法中的交叉和变异的策略，避免了陷入局部最优，加快了达到全局最优的收敛速度。此外，本文提出用权重系数来平衡各目标使各目标都能达到相对较优的效果。(Multi-objective loading of goods is a complicated combinatorial optimization problems are NP hard problems, this paper, hybrid particle swarm algorithm to solve multi-objective problem loading cargo. Hybrid Particle Swarm Algorithm in elementary particle swarm optimization based on genetic algorithm through the introduction of crossover and mutation of the strategy to avoid a fall into local optimum, global optimum to achieve accelerated convergence. In addition, this paper, the weight factor used to balance the various objectives so that the objectives can be achieved relatively better results.)

说明：  CA算法可以将数量型属性划分成若干个优化的区间，它综合了分层聚类于划分聚类的优点，对于给定的不同的初始类个数，CA算法能随着迭代过程的不断进展改变类的数目，一些竞争力差的类即类的基数小于给定阙值的类将在迭代过程中不断消失，最终得到能够有效体现数据实际分布情况的优化聚类个数。(CA algorithm for quantitative attributes can be divided into a number of the interval optimization, hierarchical clustering which combines the advantages of clustering in the division, for a given number of different types of initial, CA algorithm as the iterative process the continuous progress of change in the number of categories, some categories of poor competitiveness of the base type that is less than the value of a given category Que iterative process will continue to disappear and eventually be able to effectively reflect the actual distribution of data to optimize the number of clustering.)