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指出了非线性动态信号参数辨识的重要性；分析了目前采用的方法的不足；对非正交复Morlet小波满足Mercy条件和再生性的命题作了证明；用复Morlet小波构建出一种核函数，与主分量分析方法相结合，对非线性动态信号进行参数辨识和预测；仿真结果验证了该方法的正确性和有效性，表明该方法具有较好的理论价值和实用价值。 (Pointed out the nonlinear dynamic signals the importance of parameter identification analysis of the current shortcomings of the method used for non-orthogonal complex Morlet wavelet Mercy meet the conditions and regeneration of the proposition was made to prove using complex Morlet wavelet to build a nuclear function, with the principal component analysis approach combining nonlinear dynamic parameter identification and signal prediction simulation results verify the correctness and effectiveness, show that the method has a better theoretical value and practical value.)

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说明：  利用遗传算法、蚁群算法、PSO等对SVM模型进行优化，实现高效分类和回归预测(The SVM model is optimized by genetic algorithm, ant colony algorithm and PSO to achieve efficient classification and regression prediction.)

电容层析成像系统适用 电容层析成像 技术根据被测物质各相具有不同的介电常数，当各相组分分布或浓度分布发生变化时，将引起混合流体等价介电常数发生变化，从而使测量电极对间的电容值发生变化，在此基础上，利用相应的图像重建算法重建被测物场的介电分布图。目前电容层析成像分为圆周是和平面式两种。(The application of electrical capacitance tomography system)

说明：  matlab model of hybrid wind power plant and solar cell for hybrid power plant

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用于数据预处理的多小波分解与重构程序.其中多小波是采用CL4平衡多小波,所以分解前的预处理是采用等值预滤波器.如果要用别的非平衡多小波,只需替换为相应的预滤波器即可.-for data pretreatment multiwavelet decomposition and reconstruction process. Multiwavelet which is the Panasonic balance multiwavelets Therefore, the pretreatment before decomposition is the equivalent pre-filter. If you want other non-equilibrium multiwavelets only replacement for the corresponding pre-filter may be.

电力监控管理系统力控组态软件开发脚本源程序-Power monitoring and management system software development ForceControl configuration script source

说明：  无人机进行任务分配，采用蚁群算法，原作者位于github(The drone uses the ant colony algorithm for task allocation. The original author is located on github)

说明：  在葡萄酒制造业中，对于葡萄酒的分类具有很大意义，因为这涉及到不同种类的葡萄酒的存放以及出售价格，采用神经网络做为分类器可以有效预测相关葡萄酒的种类，从UCI数据库中得到wine数据记录的是在意大利某一地区同一区域上三种不同品种的葡萄酒的化学成分分析，数据里含有178个样本分别属于三个类别(类别标签已给)，每个样本含有13个特征分量(化学成分)，将这178个样本70%做为训练样本，另30%做为测试样本，用训练样本对神经网络分类器进行训练，用得到的模型对测试样本的进行分类标签预测。(In the wine manufacturing industry, classification of wine is of great significance, because it involves the storage and selling price of different kinds of wine. Using neural network as classifier can effectively predict the types of related wine. The wine data recorded from UCI database is the chemical composition of three different kinds of wine in the same region of Italy According to the analysis, 178 samples in the data belong to three categories (the category label has been given). Each sample contains 13 characteristic components (chemical components). 70% of the 178 samples are used as training samples, and 30% are used as test samples. The training samples are used to train the neural network classifier, and the model is used to predict the classification label of the test samples.)