regression-

matrix pencil for doa estimation using multiple and single snapshot

When capturing an iris image under unconstrained conditions and without user cooperation, the image quality can be highly degraded by poor focus, off-angle view, motion blur, specular reflection (SR), and other artifacts. The noisy iris images increase the intra-individual variations, thus markedly degrading recognition accuracy. To overcome these problems, we propose a new iris recognition algorithm for noisy iris images.

create matlab files in to .exe files in docx format read this file carefully and execute the .exe files in matlab

对于Matlab中simulink模块仿真进行了详尽的说明 主要用途集中在控制系统仿真上(This is a introduction of simulation of dynamic system based on the simulink module. This article focus on the control system.)

上課所教所有matlab 小波去燥的教程！~非常適合各位的參考！非常好喔！(Classes taught in all the matlab wavelet tutorial to dry! ~ Very suitable for your reference! Oh very good!)

无线传感器网络分簇路由算法，很经典的，适合初学者练习使用(Clustering routing algorithms in wireless sensor networks, very classic, suitable for beginners to use )

jade算法适用于盲源信号的处理分离出有用的信号(jade algorithm for blind source separation of the useful signal processing)

基于遗传算法的配电网重构，有效降低配电网网损，提高了电能质量(It is a Distribution network reconfiguration based on Genetic algorithm.)

优秀论文及配套源码。首先阐述了负荷预测的应用研究现状，概括了负荷预测的特点及其影响因素，归纳了短期负荷预测的常用方法，并分析了各种方法的优劣；接着介绍了作为支持向量机（SVM）理论基础的统计学习理论和SVM的原理，推导了SVM回归模型；本文采用最小二乘支持向量机（LSSVM）模型，根据浙江台州某地区的历史负荷数据和气象数据，分析影响预测的各种因素，总结了负荷变化的规律性，对历史负荷数据中的“异常数据”进行修正，对负荷预测中要考虑的相关因素进行了归一化处理。LSSVM中的两个参数对模型有很大影响，而目前依然是基于经验的办法解决。对此，本文采用粒子群优化算法对模型参数进行寻优，以测试集误差作为判决依据，实现模型参数的优化选择，使得预测精度有所提高。实际算例表明，本文的预测方法收敛性好、有较高的预测精度和较快的训练速度。(first expounds the recent application research of load forecasting, summarized the characteristics of load forecasting and influencing factors, summed up common methods of short-term load forecasting, and analyzed the advantages and disadvantages of each method then introduced statistical learning theory and the principle of SVM as the basis of support vector machine (SVM ) theory, SVM regression model is derived this paper adopted least squares support vector machine (LSSVM) model, according to the historical load data and meteorological data of a certain area of Zhejiang Taizhou, Analysised the various factors affecting the forecast, summed up the regularity of load change , amended "outliers" in the historical load data,the load forecasting factors to be considered were normalized. The two parameters of LSSVM have a significant impact on the model, but it is still soluted based on the experience currently. So, this paper adopted particle swarm optimization algorithm to optimized )

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