A-fuzzy-neural-network

This folder or any similar folder such as this created to hold your speech files, should be copied into the root folder C: , created on the local hard drive. Then the path will read thus C:SpeechDataTope... etc. An alternative is to modify the file setTemplates.m to change the directory as appropriate. Try to use the same microphone for training as well as for recognition to obtain improved accuracy. You might also need to tweak your sound card s mic input level. The recognition time is dependent on systems s processing power and has been observed at between 1 and 5 seconds on various x86 platforms. To speed up recognition, you can implement unconstrained DTW (this is the code portion taking most time).

利用朴实贝叶斯方法求解有关肺炎的问题。肺炎对应有四个特征：发烧、疼痛、咳嗽和血细胞异常，当确定了患肺炎与否时，四个特征条件独立。假设患肺炎与否和四个特征都可表示为Ture和False。 根据pneumonia.tex文件中的数据（500行，每行前4个数对应4个特征变量，第5个数对应患肺炎是否为真，以0表示False，1表示Ture），编写Matlab 程序 maininference.m，从example.txt中读取病人的症状信息（0表示False，1表示True，-1表示not given），并计算对应的患肺炎的可能性。所给的信息按发烧、疼痛、咳嗽、血细胞异常的顺序排列。将计算结果保存在answer.txt中。(Simple Bayesian method to solve the question of pneumonia. Pneumonia corresponding four characteristics: fever, pain, cough and blood cell abnormalities, determine the risk of pneumonia or not, the four characteristics of conditional independence. Assumptions suffering from pneumonia or not and four features can be represented as Ture and False. According to the data pneumonia.tex file (500 lines, each line of the first four numbers corresponding to the four characteristic variables, number 5 corresponds to the risk of pneumonia is true, 0 represents False, said Ture), to write Matlab program maininference. m from example.txt read the information of the patient' s symptoms (0 = False, said True, the-1 indicates not given), and to calculate corresponding to the likelihood of suffering from pneumonia. The information given by the order of fever, pain, cough, blood cell abnormalities. The calculated results is be saved in answer.txt.)

VQLBG Vector quantization using the Linde-Buzo-Gray algorithm

卡尔曼算法估计运动障碍物的轨迹程序，仿真效果良好(Kalman algorithm to estimate the trajectory of moving obstacles procedures, simulation results good)

首先定义一个高斯白噪声，然后在正弦信号中添加白噪声，再对带噪声的正弦信号进行平均值滤波(First, a Gauss white noise is defined, then the white noise is added to the sinusoidal signal, and then the average filtering of the sinusoidal signal with noise is filtered)

mat lab code for image embedding and extracting using DST

MATLAB金融工具箱，包括固定收益证券、经济经济学、金融工具、金融衍生品工具箱(MATLAB Financial Toolbox, including fixed income securities, economic economics, financial instruments, financial derivatives Toolbox)

《最优阵列信号处理》的MATLAB程序，包括所有的章节的程序("Optimal array signal processing MATLAB program)

说明：  特殊函数计算 gamafun 用逼近法计算伽玛函数的值 lngama 用Lanczos算法计算伽玛函数的自然对数值 Beta 用伽玛函数计算贝塔函数的值 gamap 用逼近法计算不完全伽玛函数的值 betap 用逼近法计算不完全贝塔函数的值 bessel 用逼近法计算伽玛函数的值 bessel2 用逼近法计算第二类整数阶贝塞尔函数值 besselm 用逼近法计算变型的第一类整数阶贝塞尔函数值 besselm2 用逼近法计算变型的第二类整数阶贝塞尔函数值 ErrFunc 用高斯积分计算误差函数值 SIx 用高斯积分计算正弦积分值 CIx 用高斯积分计算余弦积分值 EIx 用高斯积分计算指数积分值 EIx2 用逼近法计算指数积分值 Ellipint1 用高斯积分计算第一类椭圆积分值 Ellipint2 用高斯积分计算第二类椭圆积分值 (Calculation of Special Functions)

说明：  无线传感器网络定位算法MDS-MAP仿真的matlab代码(Wireless sensor network localization algorithm MDS-MAP simulation matlab code)