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神经网络理论与matlab7实现 源代码(Neural Network Theory and realization of the source code matlab7)

这本书是关于弹簧系统的，里面附有大量matlab程序，希望能看看(it is about the spring systyem)

matlab code for Fast 2D peak finder

使用MATLAB程序编写的MP4视频转换程序，支持elPCSk格式和fglkKMu格式的转换，解码时音视频数据的获取就是建立在此之上的,学习MP4的最佳程序 (Using MATLAB programming MP4 video conversion program that supportselPCSk format and formatfglkKMu conversion, access is built on top of this, the best learning program MP4 audio and video data decoding )

说明：  预处理工具包括：主成分Analysi 小号（PCA），白化，过滤：高通滤波（HPF），低通滤波（LPF），子带滤波器（巴特沃斯，切比雪夫，椭圆）用的滤波器，频率子带和可调顺序子带的数量）或用户定义的 预处理功能。 后处理工具实际上包括：通过去除不想要的组件，噪声或伪像，对原始原始数据进行压缩和重建（“清理”）。 该算法不仅可以执行ICA，还可以执行二阶统计盲源分离（BSS），稀疏分量分析（SCA），非负矩阵分解（NMF），平滑分量分析（SmoCA），因子分析（FA）和任何其他可能的矩阵X = HS + N或 Y = WX形式的因式分解，其中H = W +是混合矩阵或基本向量矩阵。X是观测数据的矩阵，S是原始数据的矩阵，N表示其他噪声的矩阵。 ICA / BSS算法是纯数学公式，功能强大，但机械程序却很复杂：机械得到最佳实施后，用户要做的工作不多。ICALAB的成功和有效使用在很大程度上取决于先验知识，常识以及对预处理和后处理工具的适当使用。(icalab toolboxes for signal process, based on the software MATLAB.)

说明：  社会保障行业研究,方法论研究,仅供参考.(Social Security industry research, methodological research is for reference only)

試做錄音.讀音訊檔 並做FFT轉換和端點偵測(介面) MATLAB程序(Try to do the recording. Reading audio files and do the the FFT conversion and endpoint detection (interface) MATLAB program)

这是关于无功优化的MATLAB源程序，做电力系统无功优化的同学可以下载看看，对你的编程肯定用好处的(This is the MATLAB optimization of reactive power source, so reactive power optimization of the students can download the look of your use of the benefits of certain programming)

Optimization of the bemorph function in the MATLAB image processing toolbox

The optimization behavior of the self-adaptation (SA) evolution strategy (ES) with intermediate multirecombination (the (=I )-SA-ES) using isotropic mutations is investigated on the general elliptic objective function. An asymptotically exact quadratic progress rate formula is derived. This is used to model the dynamical ES system by a set of difference equations. The solutions of this system are used to analytically calculate the optimal learning parameter  . The theoretical results are compared and validated by comparison with real (=I )-SA-ES runs on typical elliptic test model cases. The theoretical results clearly indicate that using a model-independent learning parameter  leads to suboptimal performance of the (=I )-SA-ES on objective functions with changing local condition numbers as often encountered in practical problems with complex fitness landscapes.