BP神经网络介绍 神经网络是机器学习中一种常见的数学模型,通过构建类似于大脑神经突触联接的结构,来进行信息处理.在应用神经网络的过程中,处理信息的单元一般分为三类:输入单元.输出单元和隐含单元. 顾名思义:输入单元接受外部给的信号与数据:输出单元实现系统处理结果的输出:隐含单元处在输入和输出单元之间,从网络系统外部是无法观测到隐含单元的结构的.除了上述三个处理信息的单元之外,神经元间的连接强度大小由权值等参数来决定. 图为BP神经网络结构:(图片均为截图来的笔记,蒟蒻手动狗头) 单个神经元的工作

欢迎大家关注我们的网站和系列教程:http://panchuang.net/ ,学习更多的机器学习.深度学习的知识! [前言]:在前面的内容里,我们已经学习了循环神经网络的基本结构和运算过程,这一小节里,我们将用TensorFlow实现简单的RNN,并且用来解决时序数据的预测问题,看一看RNN究竟能达到什么样的效果,具体又是如何实现的. 在这个演示项目里,我们使用随机生成的方式生成一个数据集(由0和1组成的二进制序列),然后人为的增加一些数据间的关系.最后我们把这个数据集放进RNN里,让RNN去

代码: % Discrete-time Signal x1(n) % Ts = 0.0002; n = -25:1:25; nTs = n*Ts; Fs = 1/Ts; x = exp(-1000*abs(nTs)); Ts = 0.001; n = -5:1:5; nTs = n*Ts; Fs = 1/Ts; x = exp(-1000*abs(nTs)); % Analog Signal Dt = 0.00005; t = -0.005:Dt:0.005; xa = x * sinc(Fs*

代码: % Analog Signal Dt = 0.00005; t = -0.005:Dt:0.005; xa = exp(-1000*abs(t)); % Discrete-time Signal Ts = 0.0002; n = -25:1:25; x = exp(-1000*abs(n*Ts)); % Discrete-time Fourier Transform %Wmax = 2*pi*2000; K = 500; k = 0:1:K; w = pi*k/K; % index ar

代码: % Analog Signal Dt = 0.00005; t = -0.005:Dt:0.005; xa = exp(-1000*abs(t)); % Continuous-time Fourier Transform Wmax = 2*pi*2000; K = 500; k = 0:1:K; % index array k for frequencies W = k*Wmax/K; % freqency between 0 and +pi, [0,pi] axis divided i

静止背景下的卡尔曼多目标跟踪 最近学习了一下多目标跟踪,看了看MathWorks的关于Motion-Based Multiple Object Tracking的Documention. 官网链接:http://cn.mathworks.com/help/vision/examples/motion-based-multiple-object-tracking.html?s_tid=gn_loc_drop 程序来自matlab的CV工具箱Computer Vision System Toolbo

最近在网上找到了一个使用LSTM 网络解决  世界银行中各国 GDP预测的一个问题,感觉比较实用,毕竟这是找到的唯一一个可以正确运行的程序. #encoding:UTF-8 import pandas as pd from pandas_datareader import wb import torch import torch.nn import torch.optim #读取数据 countries = ['BR', 'CA', 'CN', 'FR', 'DE', 'IN', 'IL', '

如下样例基于tensorflow实现了一个简单的3层深度学习入门框架程序,程序主要有如下特性: 1.  基于著名的MNIST手写数字集样例数据:http://yann.lecun.com/exdb/mnist/ 2.  加入衰减学习率优化,使得学习率可以根据训练步数指数级减少,在训练后期增加模型稳定性 3.  加入L2正则化,减少各个权重值大小,避免过拟合问题 4.  加入滑动平均模型,提高模型在验证数据上的准确性 网络一共3层,第一层输入层784个节点的输入层,第二层隐藏层有500个节点,第三

题目太长了!下载地址[传送门] 第1题 简述:识别图片上的数字. 第1步:读取数据文件: %% Setup the parameters you will use for this part of the exercise input_layer_size = 400; % 20x20 Input Images of Digits num_labels = 10; % 10 labels, from 1 to 10 % (note that we have mapped "0" to

代码: % Discrete-time Signal x1(n) : Ts = 0.0002 Ts = 0.0002; n = -25:1:25; nTs = n*Ts; x1 = exp(-1000*abs(nTs)); figure('NumberTitle', 'off', 'Name', 'Example3.23 Reconstructed From x1(n)'); set(gcf,'Color','white'); subplot(2,1,1); stairs(nTs*1000,x1

代码: % Discrete-time Signal x2(n) Ts = 0.001; n = -5:1:5; nTs = n*Ts; Fs = 1/Ts; x = exp(-1000*abs(nTs)); % Analog Signal Dt = 0.00005; t = -0.005:Dt:0.005; xa = x * sinc(Fs*(ones(length(n),1)*t - nTs'*ones(1,length(t)))) ; % check error = max(abs(xa

代码: b = [0.0181, 0.0543, 0.0543, 0.0181]; % filter coefficient array b a = [1.0000, -1.7600, 1.1829, -0.2781]; % filter coefficient array a m = 0:length(b)-1; l = 0:length(a)-1; % index arrays m and l K = 500; k = 0:1:K; % index array k for frequenci

上代码: subplot(1,1,1); b = 1; a = [1, -0.8]; n = [0:100]; x = cos(0.05*pi*n); y = filter(b,a,x); figure('NumberTitle', 'off', 'Name', 'Input and Output sequence'); set(gcf,'Color','white'); subplot(2,1,1); stem(n,x); title('Input sequence'); xlabel('n'

上代码: w = [0:1:500]*pi/500; % freqency between 0 and +pi, [0,pi] axis divided into 501 points. H = exp(j*w) ./ (exp(j*w) - 0.9 * ones(1,501)); magH = abs(H); angH = angle(H); realH = real(H); imagH = imag(H); %% ---------------------------------------

用到的性质 代码: n = -5:10; x = sin(pi*n/2); k = -100:100; w = (pi/100)*k; % freqency between -pi and +pi , [0,pi] axis divided into 101 points. X = x * (exp(-j*pi/100)) .^ (n'*k); % DTFT of x % signal decomposition [xe,xo,m] = evenodd(x,n); % even and odd

用到的性质 上代码: n = -5:10; x = rand(1,length(n)); k = -100:100; w = (pi/100)*k; % freqency between -pi and +pi , [0,pi] axis divided into 101 points. X = x * (exp(-j*pi/100)) .^ (n'*k); % DTFT of x % folding property y = fliplr(x); m = -fliplr(n); % signa

用到的性质 上代码: n = -5:10; x = rand(1,length(n)) + j * rand(1,length(n)); k = -100:100; w = (pi/100)*k; % freqency between -pi and +pi , [0,pi] axis divided into 101 points. X = x * (exp(-j*pi/100)) .^ (n'*k); % DTFT of x % conjugation property y = conj(x

用到的性质 上代码: n = 0:100; x = cos(pi*n/2); k = -100:100; w = (pi/100)*k; % freqency between -pi and +pi , [0,pi] axis divided into 101 points. X = x * (exp(-j*pi/100)) .^ (n'*k); % DTFT of x % signal multiplied y = exp(j*pi*n/4) .* x; % signal multiplied

代码: x = rand(1,11); n = 0:10; k = 0:500; w = (pi/500)*k; % [0,pi] axis divided into 501 points. X = x * (exp(-j*pi/500)) .^ (n'*k); % DTFT of x % signal shifted by two samples y = x; m = n + 2; Y = y * (exp(-j*pi/500)) .^ (m'*k); % DTFT of y = x(n-2)

上代码: x1 = rand(1,11); x2 = rand(1,11); n = 0:10; alpha = 2; beta = 3; k = 0:500; w = (pi/500)*k; % [0,pi] axis divided into 501 points. X1 = x1 * (exp(-j*pi/500)) .^ (n'*k); % DTFT of x1 X2 = x2 * (exp(-j*pi/500)) .^ (n'*k); % DTFT of x2 x = alpha *