Neural-Network-

大学里面的经典教材，浙江大学出版的概率论与数理统计(Colleges, the classic textbook, published by Zhejiang University, probability theory and mathematical statistics)

该电子书详细介绍了matlab7.1的基础知识，适合初学者！(The book details the basic knowledge matlab7.1, suitable for beginners!)

A computational method for quantitative analysis by multiwavelength absorption spectroscopy

系统地阐述了低密度校验码基于图模型的编译码思想，介绍了密度进化理论，对影响低密度校验码纠错性能的两个主要因素——度序列设计和围长设计进行了深入分析，详细介绍并设计了LDPC的编解码(Systematic exposition of the LDPC graph model based codecs idea, introduces the density evolution theory, the two main factors that affect the performance of LDPC error correction- the degree of sequence design and girth design depth analysis, detailed and designed LDPC codec)

些程序是一个雷达跟踪一目标的仿真图形,可实现kalman滤波的估计轨迹与真实轨迹的误差,并分别可绘出X,Y方向的跟踪误差,对初学都来说,是一个非常好的学习例子(These procedures is one goal of a radar tracking simulation graphics, can be realized kalman filter estimated trajectory with the true trajectory error, and can be mapped, respectively, X, Y direction of the tracking error for both novice, it is a very good learning examples)

主动轮廓模型CV算法，实现核磁共振脑实质分割(active contour CV Algorithm)

ELM 极限学习机的核函数 MATLAB程序，用于深入研究和改进极限学习机(Kernel MATLAB program ELM Extreme Learning Machine for further research and improvement Extreme Learning Machine)

iris recognition using haar wavelet

说明：  用离散正交多项式求三次拟合多项式[MATLAB版本]，希望对大家有帮助(Discrete orthogonal polynomials for three polynomial fitting [MATLAB version], in the hope that everyone has to help)

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)