Multilabel-Image-Classification-via-High

瞬时无功功率理论ip-iq法的源程序，m文件，程序有详细的解释与说明。效果比较好。可以进行谐波分析。(Instantaneous reactive power theory ip-iq method of source, m files, programs have detailed explanations and instructions. Results were better. Harmonic analysis can be performed.)

指纹识别源码---matlab版--附图指纹图像(Fingerprint identification source code--- matlab version- attached fingerprint image)

LSL最小二乘方格型算法的自适应滤波程序及其应用(LSL least-squares grid-based algorithm for adaptive filtering procedures and its application)

这是gsm系统中gsm信号数据生成的代码matlab仿真程序，上传给大家学习和参考。(This is the gsm system, gsm signal data generated code matlab simulation program, uploaded for everyone to study and reference.)

Fuzzy logic simulink

journal one for the UPFC voltage profile and power measurement

License plate identification using matlab to identify vehicles license plate

本压缩包里的文件包括MATLAB编写的数字识别程序以及所用的数字样本，供大家参考。(Number Recognition)

本程序主要利用小波分析的mallat算法实现谱分析的功能(The main advantage of wavelet analysis procedure mallat function of spectral analysis algorithms)

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).)