Description: 本算法在训练步数、训练时间及其误差精度等方面都优于常规的模糊神经网络，其学习收敛速度快、误差曲线也更稳定。-the algorithm steps in training, training time and error accuracy is superior to the conventional fuzzy neural network, learning fast convergence, the error curve is more stable. Platform: |
Size: 1641 |
Author:王鹏 |
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Description: 本人做的神经网络的实验，步骤详细，分析具体，适合做入门学习用-I do neural network experiments, the steps detailed analysis of specific, suitable for entry to study Platform: |
Size: 133598 |
Author:snow |
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Description: labwindows/CVI的虚拟仪器设计(电子版)
本书详细地介绍了应用当前信号分析与处理新技术来设计不同测量功能的虚拟仪器的工作原理和方法。内容包括虚拟仪器设计的方法和步骤，I/O接口设备的软件驱动，LabWindows/CVI与MATLAB语言的接口，以及基于自相关伪随机系统辨识、神经网络、小波变换、模糊理论等技术虚拟仪器设计的方法和技巧。 本书内容新颖丰富、论述简洁，提供了大量典型的实例。本书可作为大专院校教科书，也可作为工程技术人员和科技工作者学习设计虚拟仪器的自学用书。-labwindows / CVI virtual instrument design (electronic version) the book details on the application current signal analysis and processing technology to design new measurement functions of different virtual machines, the principle and method. Including virtual instrument design methods and steps, I / O interface of the software-driven equipment, LabWindows / CVI and MATLAB language interface, Based on the correlation of pseudo-random system identification, neural network, wavelet transform, Fuzzy theory virtual instrument technology design methods and techniques. The contents are rich novel, concise exposition, a large number of typical examples. This book tertiary textbooks can also be used as engineering and technical workers should study the design of virtual instrument self-learning bo Platform: |
Size: 7969758 |
Author:李新平 |
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Description: 本算法在训练步数、训练时间及其误差精度等方面都优于常规的模糊神经网络，其学习收敛速度快、误差曲线也更稳定。-the algorithm steps in training, training time and error accuracy is superior to the conventional fuzzy neural network, learning fast convergence, the error curve is more stable. Platform: |
Size: 1024 |
Author:王鹏 |
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Description: 本人做的神经网络的实验，步骤详细，分析具体，适合做入门学习用-I do neural network experiments, the steps detailed analysis of specific, suitable for entry to study Platform: |
Size: 133120 |
Author:snow |
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Description: labwindows/CVI的虚拟仪器设计(电子版)
本书详细地介绍了应用当前信号分析与处理新技术来设计不同测量功能的虚拟仪器的工作原理和方法。内容包括虚拟仪器设计的方法和步骤，I/O接口设备的软件驱动，LabWindows/CVI与MATLAB语言的接口，以及基于自相关伪随机系统辨识、神经网络、小波变换、模糊理论等技术虚拟仪器设计的方法和技巧。 本书内容新颖丰富、论述简洁，提供了大量典型的实例。本书可作为大专院校教科书，也可作为工程技术人员和科技工作者学习设计虚拟仪器的自学用书。-labwindows/CVI virtual instrument design (electronic version) the book details on the application current signal analysis and processing technology to design new measurement functions of different virtual machines, the principle and method. Including virtual instrument design methods and steps, I/O interface of the software-driven equipment, LabWindows/CVI and MATLAB language interface, Based on the correlation of pseudo-random system identification, neural network, wavelet transform, Fuzzy theory virtual instrument technology design methods and techniques. The contents are rich novel, concise exposition, a large number of typical examples. This book tertiary textbooks can also be used as engineering and technical workers should study the design of virtual instrument self-learning bo Platform: |
Size: 7969792 |
Author:李新平 |
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Description: This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.-This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar-xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. Platform: |
Size: 220160 |
Author:晨间 |
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Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhart博士和kennedy博士发明。源于对鸟群捕食的行为研究
PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍
同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), has Dr. Eberhart and Dr. kennedy invention. Deriving from the behavior of birds of prey PSO with genetic algorithm is similar to an iterative optimization-based tools. System initialization for a group of random solutions, through the iterative search for optimal values. But there is no cross-genetic algorithm used (crossover) and mutation (mutation). But the particles in the solution space of the particles to follow the optimal search. In detail the steps after the introduction sections compared with the genetic algorithm, PSO has the advantage of being simple and easy and did not realize many of the parameters need to be adjusted. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications Platform: |
Size: 22528 |
Author:zzh |
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Description: Matlab手写文字识别源代码
这个Demo展示手写文字识别的各个步骤，包括图像的预处理、裁剪、大小转换、人工神经网络训练和识别等等。-Handwritten character recognition Matlab source code of this Demo display of handwritten character recognition of the various steps, including image preprocessing, cutting, size conversion, artificial neural network training and recognition and so on. Platform: |
Size: 102400 |
Author:lcp |
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Description: 神经网络的程序,有详细的步骤,对初学者有很大帮助-Neural network program, there are detailed steps are very helpful for beginners Platform: |
Size: 15360 |
Author:liyan |
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Description: 使用说明
第一步：训练网络。使用训练样本进行训练。
第二步：识别。首先，打开图像（256色）；再次，进行归一化处理，点击“一次性处理”；最后，点击“R”或者使用菜单找到相应项来进行识别。识别的结果显示在屏幕上，同时也输出到文件result.txt中。
该系统的识别率一般情况下为90 。
此外，也可以单独对打开的图片一步一步进行图像预处理工作，但要注意，每一步工作只能执行一遍，而且要按顺序执行。
具体步骤为：“256色位图转为灰度图”－“灰度图二值化”－“去噪”－“倾斜校正”－“分割”－“标准化尺寸”－“紧缩重排”。
注意，待识别的图片要与win.dat和whi.dat位于同一目录，这两文件保存训练后网络的权值参数。-Help
The first step: Training Network. The use of training samples for training.
Step two: identification. First, open the image (256 colors) again, normalized to deal with, click on the "one-time deal" Finally, click "R" or use the menu to find the corresponding items to be identified. Recognition results show up on the screen, but also output to a file Result.txt Medium.
The system s recognition rate under normal circumstances was 90 .
Alternatively, you could open a separate picture of the image pre-processing step by step job, but bearing in mind that each step can only run job again, but according to the order of implementation.
Concrete steps as: "256-color bitmap to grayscale"- "two grayscale values of"- "De-noising"- "tip-tilt correction"- "split"- "the standardization of size"- "tight rearrangement."
Note that to be identified with the picture win.dat and whi.dat located in the same directory, these two files after training the weights of the netw Platform: |
Size: 64512 |
Author:李晓国 |
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Description: BP 网络进行0 - 9 十个数字图像的识别
在BP 算法和数字图像处理技术的基础上, 本文在MATLAB 软件环境下提取Word 文档中26 种字体的
0 - 9 十个数字的图像, 使用MATLAB 神经网络工具箱及图像处理工具箱进行数字的识别, 给出了较详细的处理
步骤及相关程序, 并比较了各种识别算法的收敛速度和识别率。-BP network of 10 digital images 0_9 identification in BP algorithm and digital image processing technology, based on the MATLAB software environment In this paper, extraction of Word documents under the 26 kinds of fonts in the 0- 9 of 10 digital images, using the MATLAB neural network tool boxes and digital image processing toolbox of identification, are given more detailed processing steps and related procedures, and compare a variety of identification algorithms and identify the rate of convergence speed. Platform: |
Size: 457728 |
Author:Jack |
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Description: Research on Video Caption Detection and Extraction. The automatic video annotation is a key module of the video indexing and retrieval system,and the extraction and location of captions are important steps for video annotation.An automatic extraction algorithm for video caption by combining the wavelet transform and the fuzzy clustering neural network in presented.This algorithm is especially applicable to extracting and locating of Chinese captions.The theoretical reachs show that the proposed algorithm can achieve an accuracy in more than 90 in caption detection and location.After the selected characters in the region to identify with OCR technology,OCR recognition accuracy can reach 99 .-Research on Video Caption Detection and Extraction. The automatic video annotation is a key module of the video indexing and retrieval system,and the extraction and location of captions are important steps for video annotation.An automatic extraction algorithm for video caption by combining the wavelet transform and the fuzzy clustering neural network in presented.This algorithm is especially applicable to extracting and locating of Chinese captions.The theoretical reachs show that the proposed algorithm can achieve an accuracy in more than 90 in caption detection and location.After the selected characters in the region to identify with OCR technology,OCR recognition accuracy can reach 99 . Platform: |
Size: 13312 |
Author:段军伟 |
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Description: neural-network模型中，在產生一個輸出值前units轉換它們的net-input數值為一個activation value並視為一個中介的步驟。很多架構省略這個中介的步驟並且直接到輸出值的產生。在這裡，先忽略這個activation value的複雜度，我們首要的工作是output value輸出值的產生。我們以一個微分方程式的形式來表示一個unit的output value。就好像是生物學中所提的同等事物一樣，units的輸出值是時間的動態函數。-neural-network model, generating an output value before the units converted to their net-input values for an activation value, and as an intermediate step. Many architecture omitted intermediate steps and go directly to the output value of production. Here, first, ignore the activation value of the complexity, our primary task is to output value output value generation. We are in the form of a differential equation to represent a unit of output value. Biology is like the same things mentioned in the same, units of output value is the dynamic function of time. Platform: |
Size: 79872 |
Author:曾琪騰 |
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Description: 用Visual Basic--实现BP神经网络语言,只要介绍一些必要算法以及步骤-Using Visual Basic- to achieve BP neural network language, as long as necessary to introduce some algorithms and steps!! Platform: |
Size: 79872 |
Author:lyong88 |
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Description: RNNSIM ver. 1.0 is a program with an intercative graphical user interface
(GUI) that runs under MATLAB ver. 5.0 or higher. The program can be used
in training and testing the Random Neural Network(RNN) models.
This version (ver. 1.0) implements only the 3 layer feed forward RNN model.
In the next versions, the multi hidden layers and the recurrent RNN models
can be implemented. To obtain faster training, the training section can be
written as a MEX file and invoked from the GUI.
If you have the m files in the directory rnnsim for example, then you can
run the program following the next steps:
1- run MATLAB as usual
2- from the MATLAB command window, write cd rnnsim
3- from the MATLAB command window, write rnnsim- RNNSIM ver. 1.0 is a program with an intercative graphical user interface
(GUI) that runs under MATLAB ver. 5.0 or higher. The program can be used
in training and testing the Random Neural Network(RNN) models.
This version (ver. 1.0) implements only the 3 layer feed forward RNN model.
In the next versions, the multi hidden layers and the recurrent RNN models
can be implemented. To obtain faster training, the training section can be
written as a MEX file and invoked from the GUI.
If you have the m files in the directory rnnsim for example, then you can
run the program following the next steps:
1- run MATLAB as usual
2- from the MATLAB command window, write cd rnnsim
3- from the MATLAB command window, write rnnsim
Platform: |
Size: 63488 |
Author:hacen |
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Description: BP神经网络在图像处理中的应用，及介绍处理的方法和编程的步骤。-The BP neural network application in image processing, and introduces the method and the programming of processing steps.
Platform: |
Size: 33792 |
Author:唐福菊 |
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Description: 该代码是通过MATLAB实现的神经网络的数字识别。在压缩包中包含具体的操作步骤，相关代码，和图片。具体是通过鼠标选择的方法，对数字进行识别。-The code is a digital identification through MATLAB neural network. Contains specific steps in the compression package, code and pictures. Specifically by the mouse to select the method of the digital identification. Platform: |
Size: 308224 |
Author:宫贺 |
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Description: 遗传算法的基本程序，主要的步骤，举例说明怎么应用遗传算法优化BP神经网络-GA basic procedures, main steps, illustrate how the application of genetic algorithm to optimize BP neural network Platform: |
Size: 54272 |
Author:sunyanchao |
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Description: 本文为一款基于BP神经网络的数据处理源代码，并配有详细步骤解释-This article is a based on BP neural network data processing source code, and with a detailed explanation of the steps Platform: |
Size: 2048 |
Author:my name |
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Search - neural network STEPS