FDM解常微分方程 问题描述 \[\frac{d^2\phi}{dx^2}=S_{\phi} \tag{1} \] 这是二阶常微分方程(second-order Ordinary Differential Equation, ODE),考虑最简单的情况即\(S=0\),积分后可得\(\phi=c_1x+c_2\),有两个待定系数,因此要求解该方程必须提供两个边界条件(因为方程中不包含时间项,因此无初始条件),例如 \[\phi(x_L)=\phi_L \quad \phi(x_R)=\phi_R

5 Neural Networks (part two) content: 5 Neural Networks (part two) 5.1 cost function 5.2 Back Propagation 5.3 神经网络总结 接上一篇4. Neural Networks (part one).本文将先定义神经网络的代价函数,然后介绍逆向传播(Back Propagation: BP)算法,它能有效求解代价函数对连接权重的偏导,最后对训练神经网络的过程进行总结. 5.1 cost func

this blog from: http://blog.demofox.org/2017/03/09/how-to-train-neural-networks-with-backpropagation/ How to Train Neural Networks With Backpropagation Posted on March 9 2017 by Demofox This post is an attempt to demystify backpropagation, which is t

Given an unsorted integer array, find the first missing positive integer. For example,Given [1,2,0] return 3,and [3,4,-1,1] return 2. Your algorithm should run in O(n) time and uses constant space. 这道题求的是在一个乱序数组中缺少的第一个正数,要求时间复杂度o(n)空间复杂度为1. 先上代码 pack

Evolution Strategies as a Scalable Alternative to Reinforcement Learning this blog from: https://blog.openai.com/evolution-strategies/   MARCH 24, 2017 Evolution Strategies as a Scalable Alternative to Reinforcement Learning We’ve discovered that evo

1. Feedforward and cost function; 2.Regularized cost function: 3.Sigmoid gradient The gradient for the sigmoid function can be computed as: where: 4.Random initialization randInitializeWeights.m function W = randInitializeWeights(L_in, L_out) %RANDIN

Create options struct for optimization functions. optimset('parameter', value, ...); %设置所有参数及其值,未设置的为默认值 optimset(optimfun); %设置与最优化函数有关的参数为默认 optimset(old, 'parmeter', 'value', ... ); %复制已有的设置,并修改特定项 optimset(old, new); %两项合并 GradObj When set to "on

2.1 Inclusion-Exclusion Roughly speaking, a "sieve method" in enumerative combinatorics is a method for determining the cardinality of a set $S$ that begins with a larger set and somehow subtracts off or cancels out unwanted elements. Sieve meth

package Main; import java.io.InputStream; import java.util.Scanner; public class Main { public static long constant = 1000000007; public static void main(String[] args) { Scanner in = new Scanner(System.in); long k = in.nextLong(); long l = in.nextLo

前馈神经网络(Feedforward Neural Network - BP) 常见的前馈神经网络 感知器网络 感知器(又叫感知机)是最简单的前馈网络,它主要用于模式分类,也可用在基于模式分类的学习控制和多模态控制中.感知器网络可分为单层感知器网络和多层感知器网络. BP网络 BP网络是指连接权调整采用了反向传播(Back Propagation)学习算法的前馈网络.与感知器不同之处在于,BP网络的神经元变换函数采用了S形函数(Sigmoid函数),因此输出量是0~1之间的连续量,可实现从输入到

瘋耔C++笔记 欢迎关注瘋耔新浪微博:http://weibo.com/cpjphone 参考:C++程序设计(谭浩强) 参考:http://c.biancheng.net/cpp/biancheng/cpp/rumen_8/ 博客原文:http://www.cnblogs.com/Ph-one/p/3974707.html 一.C++初步认识 1.C++输入.输出.头文件解释 #include<iostream> using namespace std ; int mian() { cout

原本使用正常的情况, 切换为测试库突然出现这个错误, 网上搜索并排查后得出导致这个问题的原因: (1)就是你的除数为0(2)除数或者被除数为null 找出你出错的界面, 并打断点, 看看是否出现上面两种情况即可...

前言: CNN作为DL中最成功的模型之一,有必要对其更进一步研究它.虽然在前面的博文Stacked CNN简单介绍中有大概介绍过CNN的使用,不过那是有个前提的:CNN中的参数必须已提前学习好.而本文的主要目的是介绍CNN参数在使用bp算法时该怎么训练,毕竟CNN中有卷积层和下采样层,虽然和MLP的bp算法本质上相同,但形式上还是有些区别的,很显然在完成CNN反向传播前了解bp算法是必须的.本文的实验部分是参考斯坦福UFLDL新教程UFLDL:Exercise: Convolutional Ne

02-线性结构1. 一元多项式求导 (25) 设计函数求一元多项式的导数.(注:xn(n为整数)的一阶导数为n*xn-1.) 输入格式:以指数递降方式输入多项式非零项系数和指数(绝对值均为不超过1000的整数).数字间以空格分隔. 输出格式:以与输入相同的格式输出导数多项式非零项的系数和指数.数字间以空格分隔,但结尾不能有多余空格.注意“零多项式”的指数和系数都是0,但是表示为“0 0”. 输入样例: 3 4 -5 2 6 1 -2 0 输出样例: 12 3 -10 1 6 0 最简单的方式是用

Musical Theme Time Limit: 1000MS   Memory Limit: 30000K Total Submissions: 25348   Accepted: 8546 Description A musical melody is represented as a sequence of N (1<=N<=20000)notes that are integers in the range 1..88, each representing a key on the

永恒的大牛,kuangbin,膜拜一下,Orz 链接:http://www.cnblogs.com/kuangbin/archive/2013/04/23/3039313.html Musical Theme Time Limit: 1000MS   Memory Limit: 30000K Total Submissions: 14334   Accepted: 4945 Description A musical melody is represented as a sequence of

Differences Between Xcode Project Templates for iOS Apps When you create a new iOS app project in Xcode, you get to choose between several project templates, from the aptly named “Empty Application” to specialized things like an “OpenGL Game”. I noti

题目链接: 题目 I. Approximating a Constant Range time limit per test:2 seconds memory limit per test:256 megabytes 问题描述 When Xellos was doing a practice course in university, he once had to measure the intensity of an effect that slowly approached equilibr

Cow Contest 题目链接: http://acm.hust.edu.cn/vjudge/contest/122685#problem/H Description N (1 ≤ N ≤ 100) cows, conveniently numbered 1..N, are participating in a programming contest. As we all know, some cows code better than others. Each cow has a certa

B. Approximating a Constant Range When Xellos was doing a practice course in university, he once had to measure the intensity of an effect that slowly approached equilibrium. A good way to determine the equilibrium intensity would be choosing a suffi

matlab求极限(可用来验证度量函数或者隶属度函数)可用来验证是否收敛,取值范围等等. 一.问题来源 搜集聚类资料时,又看到了隶属度函数,没错,就是下面这个,期间作者提到m趋于2是,结果趋于1,我想验证下,于是查资料. 二.不同类型的极限 2.1 基础知识 a./b表示常数a除以矩阵b中每个元素或者矩阵a除以矩阵b对应元素或者常数b:点乘方a.^b,矩阵a中每个元素按b中对应元素乘方或者b是常数. 2.2 单变量独立式子 独立式子之地的是不存在连加之类的操作. 问题:用MATLAB求(x^2+