bp video

The parameters that need to be debugged in the classic incremental PID algorithm are KP,KI,KD. The three parameters are regulated by the BP neural Network, with X (i) as the input layer and the middle layer as the Simoid function: f (x) = Tanh (x) = (exp (x)-exp (-X))/(exp (x) +exp (-X)). and modify the parameters by gradient descent method Key code:%output LayerFor J=1:1:outDK (j) =2/(exp (j) +exp (K (j))) ^2;EndFor L=1:1:outDELTA3 (L) =error (k)

%2015.04.26 Kang yongxin----v 2% completion of the operation of the BP algorithm, the batch method to update the weight%%% input data format%x Matrix: Sample number * Feature dimension%y Matrix: Number of Samples * category number (in 01000 form) Close All;c Lear ALL;CLC; load data.mat;%x_test=x (1:3:30,:);% leave part of the original data as a test sample y_test=y (1:3:30,:); x_train=[x (2:3:30,:); X ( 3:3:30,:)];%x (1:2:30,:);%[x (11:25,:); x (26:30

BP wrote a simple three-lane parkour 650) this.width=650; "Src=" http://s2.51cto.com/wyfs02/M01/7E/8B/wKiom1cDkUGjC_-8AAA4Kuke2-c015.jpg " Title= "2) q9229yg$4e6fyb6%~1[2t.jpg" style= "Float:none; alt=" Wkiom1cdkugjc_-8aaa4kuke2-c015.jpg "/>650) this.width=650; "src=" Http://s2.51cto.com/wyfs02/M00/7E/87/wKioL1cDkeuCNzUCAAD5QiVZGnA493.png "style=" float: none; "title=" 1bc9e48e-21ed-4164-a70e-8ba74d37316d.png "alt=" Wkiol1cdkeucnzucaad5qivzgna493.png

Each action of the game sends data to the server. When packet sending data is intercepted, you can know what data each action sends to the server. Append the game with OD and input the command BP send for example. First, throwing an underground item. Then enter the command BP send. At this time, use the mouse to pick up items. Od will be interrupted. Press Ctrl + F9 three times, and F8 will return to

Python-based three-layer BP neural network algorithm example, pythonbp This example describes the three-layer BP neural network algorithm implemented by Python. We will share this with you for your reference. The details are as follows: This is a very nice python implementation of a layer-3 back-propagation neural network. Next I am going to try to change it to a multi-layer back-propagation neural network.

is done, i.e..The BP algorithm is rewritten as follows:1. Forward feed propagation to calculate the activation values for all nodes in each layer2. Residuals for node i of the output layer (the first nl layer):3. For4. Calculate the partial derivative:Note: In steps 2nd and 3rd above, we need to calculate it for each node i . The assumption is that the sigmoid activation function, which has stored the activation values of all the nodes during forw

the design of BP Neural network should pay attention to the following several questions: 1. Number of layers of the network. The general three-layer network structure can approximate any rational function. Although the increase of network layer can improve the precision of calculation and reduce the error, it also complicates the network and increases the training time. If you really want to increase the number of layers, you should give priority to i

Although the neural network has a very complete and useful framework, and BP Neural network is a relatively simple and inefficient one, but for the purpose of learning to achieve this neural network is still meaningful, I think. The following program uses the iris dataset, in order to facilitate the drawing first with PCA to the data to reduce the dimension. At the same time, the classification results are labeled, according to the characteristics of

Http://www.cnblogs.com/biaoyu/archive/2015/06/20/4591304.html A detailed explanation of the derivation process of BP neural network BP algorithm is one of the most effective multilayer neural network learning methods, its main characteristics is the signal forward transmission, and error transmission, through the constant adjustment of network weights, so that the final output of the network and the desir

BP (back propagation) network is the 1986 by the Rumelhart and McCelland, led by the team of scientists, is an error inverse propagation algorithm training Multilayer Feedforward Network, is currently the most widely used neural network model. BP network can learn and store a large number of input-output pattern mapping relationships without revealing the mathematical equations that describe the mapping rel

// BP neural network algorithm implementation

This article is mainly for you to introduce the Python implementation of Neural Network (BP) algorithm and simple application, with a certain reference value, interested in small partners can refer to In this paper, we share the specific code of Python to realize the neural network algorithm and application, for your reference, the specific content is as follows First, use Python to implement a simple neural network algorithm: Import numpy as np# de

Q: What do "bn *" and "BP *" stand for in frameworks/base/include/utils/iinterface. h? I understand that "B" is for binder but what about "N" and "P "? It seems like "p" may stand for "remote" and "N" for "native" but I wowould love a clarification. A: "N" is native, that is the class you inherit from to implement the interface; "P" is proxy, that is the class that is created to perform interface CILS through IPC. Http://groups.google.com/grou

The classical BP network has the following specific structure:Please pay special attention to some of the symbols shown in the diagram above:two. Learning algorithm 1. Forward transmission of signals pay special attention to the subscript in the above formula, where the weight matrix contains the bias of the neuron node itself, so the weight matrix has a column. 2. Error reversal conduction process three. A recursive formula can be used to describe th

BpNet. h: interfacefortheBpclass. E-Mail: zengzhijun369@163.com ** # includestdafx. h # includeBpNet. h # includemath. h # ifdef_DEBUG # undefTHIS_FILEstaticchar // BpNet. h: interfacefortheBpclass. /// E-Mail: zengzhijun369@163.com /**//////////////////////////////////// /// // # include stdafx. h # include BpNet. h # include math. h # ifdef_DEBUG # undef THIS_FILE static char // BpNet. h: interface for the Bp class.//// E-mail: zengzhijun369@16

. The first method is to start teaching process with large value of the parameter. While weights coefficients was being established the parameter is being decreased gradually. The second, more complicated, method starts teaching with small parameter value. During the teaching process the parameter is being increased when the teaching are advanced and then decreased again in the Final stage. Starting teaching process with low parameter value enables to determine weights coefficients signs. Refere

simple momentum-adaptive learning rate algorithm.Its iterative formula is as follows:$ $W (t+1) =w (t) +\delta W (t) $$$$\delta W (t) =rate (t) (1-moment (t)) G (t+1) +moment (t) \delta W (t-1) $$$ $rate (t+1) =\begin{cases} rate (t) \times 1.05 \mbox{if} cost (t) $ $moment (t+1) =\begin{cases} 0.9 \mbox{if} cost (t) The sample code is as follows:public class Momentadaptlearner implements learner {Net net;double moment = 0.9;double LMD = 1.05;double Precost = 0;doubl E eta = 0.01;double curre

,In the above formula, the * number is the convolution operation, the kernel function k is rotated 180 degrees and then the error term is related to the operation, and then summed.Finally, we study how to calculate the partial derivative of the kernel function connected with the convolution layer after obtaining the error terms of each layer, and the formula is as follows.The partial derivative of the kernel function can be obtained when the error item of the convolution layer is rotated 180 deg

bp neural network algorithm #include

As mentioned above, "The basic BP algorithm" preference "after the sample, so that the subsequent sample on the network more significant" This article will record how to eliminate this effect Used (x1,y1), (x2,y2),.... The total effect of (Xs,ys) is lost to the w^ (1), w^ (2),... w^ (L) w^ (k) ij=∑ pw^ (k) IJ Just replaced the original simple modifier weight matrix that part The specific algorithm flow is as follows: 1 for k-1 to L do 1.1 Initi