short-termloadforecastingwithchaostimeseries

说明：  模式识别knn分类器，matlab代码，k近邻算法(knn classifier ，matlab code)

ISI信道下的信号检测和译码的联合迭代算法的实现，编码使用递归卷积码，译码算法为BCJR算法。(Iterative algorithm to achieve joint signal detection and decoding under ISI channel, encoded using recursive convolutional code and decoding algorithm is BCJR algorithm.)

一种时频域联合捕捉主动声纳信号的方法.介绍了一种可应用于被动声纳的时频域联合捕捉主动声纳信号的方法及其工作流程。仿真相同噪声环境下使用该方法的发现距离与主动声纳探测距离,仿真结果表明使用该方法的发现距离远超过主动声纳探测距离,使用该方法可实现对目标的远程发现。(A time-frequency domain joint capture of active sonar signals. Introduced passive sonar, which can be applied when the frequency domain joint to capture active sonar signal its workflow. The discovery of the method used in the simulation of the same noise environment distance active sonar detection range, simulation results show that using the method found distance than active sonar detection range, the use of this method can be found remote target.)

灰色预测模型，详细介绍了灰色预测的理论及前景(Grey forecasting model, described in detail the theory and prospects Grey)

混沌时间序列 GP方法求关联维数和嵌入维数(Chaotic time series GP method )

calculate voltage profile for power systems in matlab M-file

matlab时频分析工具箱时频分析工具箱time-frequency analysis toolbox 第二代，比第一代增加了许多信号处理的函数，有些函数进行了功能的增进。使用的时候，解压到MATLAB安装目录的toolbox目录下，并在matlab中将其添加到搜索路径中使用。(time-frequency analysis toolbox matlab second generation than the first generation adds many signal processing functions, some functions have been enhancing features. Use it, extract it to MATLAB installation directory toolbox directory and search paths to work.)

this is average filter .raw and .bmp file converter.

DOA估计的又一经典算法，和MUSIC相比，不需要谱峰搜索，相对更快捷！是利用空间旋转不变性求得波大方向。(DOA estimation is a classical algorithm, and compared to MUSIC, without spectral peak searching, relatively more efficient! Is the use of space rotation invariance to obtain the wave direction.)

说明：  高斯过程(GP)模型中推理和预测的实现。它实现了在《Rasmussen & Williams:机器学习的高斯过程》(麻省理工学院出版社，2006)和《Nickisch & Rasmussen:二进制高斯过程分类的近似》(JMLR, 2008)中讨论的算法。该函数的优点在于灵活性、简单性和可扩展性。该函数具有一定的灵活性，首先通过定义均值函数和协方差函数来确定遗传算法的性质。其次，它允许指定不同的推理过程，如精确推理和期望传播(EP)。第三，它允许指定似然函数，如高斯函数或拉普拉斯函数(用于回归)和累积逻辑函数(用于分类)。简单性是通过一个简单的函数和紧凑的代码实现的。可扩展性是通过模块化设计来保证的，允许为已经相当广泛的推理方法、均值函数、协方差函数和似然函数库轻松添加扩展。(Gaussian Processes for Machine Learning , the MIT press, 2006 and Nickisch & Rasmussen: Approximations for Binary Gaussian Process Classification , JMLR, 2008. The strength of the function lies in its flexibility, simplicity and extensibility. The function is flexible as firstly it allows specification of the properties of the GP through definition of mean function and covariance functions. Secondly, it allows specification of different inference procedures, such as e.g. exact inference and Expectation Propagation (EP). Thirdly it allows specification of likelihood functions e.g. Gaussian or Laplace (for regression) and e.g. cumulative Logistic (for classification). Simplicity is achieved through a single function and compact code.)