snakeplannig

Time-frequency representations with fixed windows or kernels figure prominently in many applications, but perform well only for limited classes of signals.

混沌吸引子的MATLAB主程序，经验证可以使用。(Chaotic attractor MATLAB main program, you can use the experience card.)

用MATLAB程序实现support vector machine(MATLAB program support vector machine)

Return Sparc trap low-level code.

explain the coding and decoding of FEC

说明：  this is good code in matlab

各种数字基带的调制调频,就是调幅啊，很简单的啊等等 (DSB modulation for sine signal，is it ok ,so easy.)

说明：  光伏电池simulink模型，分析了光照强度和温度变化时，功率的变化情况(Simulink model of photovoltaic cell)

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 )

说明：  介绍灰色系统的相关知识，有助于提高MATLAB对它的求解(Introduction gray system knowledge, contribute to its solution MATLAB)