GSC1

matlab program numerical methods

用matlab编写了20几个常见的技术指标，方便大家开发策略时候，直接调用。 1）编写这些函数不是技术活，完全是体力活 2）之所以编写这些函数，一来是自己研究所需，二来自己编写过程中能够感悟到蕴含在公式中的思想 3）几乎所有函数，我都用数据测试过，并和交易开拓者、同花顺等计算的值进行了比对，最大程度 上保证指标算法的准确性。虽然已近极力避免错误，但是还是可能出现一些这样那样的错误，所以 请批判性使用这些函数 4）如果对这些函数有疑问（比如有些指标可能会有不同的处理方法），请参见函数中的参考文献部分 5）如果您的策略用到了这些函数，请一定研究这些函数的编写过程，避免未来函数、偷价漏价对你造成的干扰。(20 using matlab few common technical indicators, to facilitate the development of strategies when called directly. 1) preparation of these functions are not technology live, is completely manual labor 2 thoughts) reason to write these functions, one is required for their own research, and secondly to be able to comprehend the process of writing their own inherent in the formula 3) Almost all of the functions that I have tested with the data, and the Trail Blazers and trading, such as flush with the calculated values were compared right, the greatest degree of     On indicators to ensure the accuracy of the algorithm. Although almost try to avoid mistakes, but still may be some kind or another error,     Please use these critical functions 4) If you have questions about these functions (for example, some indicators may have different processing methods), see the References section function 5) If your policy to use these functions, be sure to study the process of)

已知系统的差分方程，用matlab实现输出信号的自相关计算并画出图形。(Differential equations known system, the output signal using matlab autocorrelation calculated and drawn graphics.)

ASK, OOK, FSK, BPSK, QPSK, 8PSK modulations MATLAB release MATLAB 7.1.0 (R14SP3)(ASK, OOK, FSK, BPSK, QPSK, 8PSK modulations)

说明：  基于MATLAB的FFT变换， 可用于OFDM信道中(MATLAB' s FFT-based transform, can be used for OFDM channel)

遥感数字图像处理实习报告含Matlab处理代码,有非常详细的代码和事例(Remote sensing digital image processing internship report containing Matlab handling code)

clustering matlab code,check the number of clusters alive at certain iterations

同样的MATLAB 教程 我想对学习MATLAB的人会有帮助的(The same MATLAB Tutorial MATLAB I would like to learn who will help)

基于matlab上开发的关于人工神经网络的线性神经网络的程序例子(Matlab based on the development of artificial neural networks on a linear neural network program examples)

We present in this paper a new method for blind source separation which is adapted to the case where the sources have different morphologies. We show that the morphology diversity concept leads to a new and very efficient method, even in the presence of noise.