newton

using dbscan to clustering in matlab

This file is used to generate wireless microphone signal. These models of wireless microphone signal are useful in an evaluating the performance of spectrum sensing scheme.

this is decision tree ID3 algorithm, this algorithm is one of decision tree algorithm like cart, chaid, c4.5, etc

Matlab平台DSP自动代码生成技术研究(Research on DSP automatic code generation technology with Matlab platform)

在最佳位置和合同双层办法 分布式发电的径向分布定价 使用混合整数线性规划系统(Bilevel approach for optimal location and contract pricing of distributed generation in radial distribution systems using mixed-integer linear programming)

very good files for matlab beginners very good file in different fields

说明：  这是《matlab小波分析》第四章例子的源代码！(This is the &quot matlab wavelet analysis,&quot Chapter IV of the source code examples!)

用db5小波分解嗲能质量信号 并低频部分高频进行重构 最后给出图像(Db5 can whine with the quality of the signal wavelet decomposition and reconstruction low-frequency part of the high-frequency image is given)

audio card enumerate searching ok. for multimedia developing project. thank you

GM(1,1)模型1-4 1：GM(1,1)模拟模型，在matlab中的输入方法为gm1（x），x指要模拟的序列。 2：GM(1,1)预测模型，在matlab中的输入方法为gm2（x，K），x指要模拟的序列，K指从以后序列第一个数据算起的第k个待预测数据。 3：GM(1,1)群模拟模型，在matlab中的输入方法为gm3（x），x指要模拟的序列。 4：GM(1,1)群预测模型，在matlab中的输入方法为gm4（x，K），x指要模拟的序列，K指从以后序列第一个数据算起的第k个待预测数据。 gm4对序列趋势比较好的数据预测效果较好，对上下变动的数据，特别是后4个数据趋势跟前面的数据相反的，预测效果很差。 gm2对上下变动的数据，预测效果比gm4好，但对趋势较好的数据，预测精度没有gm4高。 gm3比gm1模拟精度要高。 可以以x=[1 3 5 7 9 11 13 15]进行实验。x输入默认行向量。 所有程序在matlab6.0上调试通过。(GM (1,1) 1-4 1 : GM (1,1) simulation model Matlab in the input method for gm1 (x), x entail simulation sequence. 2 : GM (1,1) model, in the Matlab input method for gm2 (x, K), x entail simulated sequence, from the K refers to the first series after a run of data k pending forecast data. 3 : GM (1,1)- simulation model, in the Matlab input method for gm3 (x), x entail simulation sequence. 4 : GM (1,1)- forecasting model, in the Matlab input method for gm4 (x, K), x entail simulated sequence, from the K refers to the first series after a run of data k pending forecast data. Gm4 right sequence relatively good trend data predicted better results, the next change in the data, especially after four data with the previous trend data contrary, predict poor. Gm2 next to the change in forecast resu)