求一篇人工神经网络在模式识别中的应用的论文。

1. 求一篇人工神经网络在模式识别中的应用的论文。

要英文的还是中文的？

英文的话有本书：Neural Networks for Pattern Recognition
作者Christopher M. Bishop,算是这个领域中一本非常有名的著作吧
电子版可下：http://www.ulb.tu-darmstadt.de/tocs/198320841.pdf

论文的话,找了个，你看看行不？
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=41401

2. 第一篇提出BP神经网络的论文是哪一篇？

最初是86年，Rumelhart和McCelland领导的科学家小组在《平行分布式处理》一书中，对具有非线性连续变换函数的多层感知器的误差反向传播BP算法进行了详尽的分析，实现了Minsky关于多层网络的设想。
一般引用的话，无需引用第一篇，只需引用介绍BP网络的文献即可。最开始的文献往往理论不完善。反而阅读意义不大。

3. 求一篇关于bp神经网络的外文 我做毕业论文做外文翻译用的 翻译过来有2000字就可以

推荐到OA图书馆查询。中英文都有。
英文文献，输入英文关键词即可。

4. 求一篇神经网络控制方面的论文 有的朋友请发在我的邮箱chen1989ming@163.com 急用呀 谢谢了哟！

你是什么专业的，神经网络的使用方向可是多元的，你要在什么方向上的控制问题？

5. 神经网络方向的论文那些核心期刊比较好重呢

首先你得清楚自己写的论文是属于哪一类的小分支，然后对应的在网上找下这样的期刊呗，比如（国际神经精神科学杂志）……这些都是需要自己多了解的吧~

6. 求一篇关于神经网络的英文翻译

Introduction 
--------------------------------------------------------------------

“神经网络”这个词实际是来自于生物学，而我们所指的神经网络正确的名称应该是“人工神经网络（ANNs）”。在本文，我会同时使用这两个互换的术语。

一个真正的神经网络是由数个至数十亿个被称为神经元的细胞（组成我们大脑的微小细胞）所组成，它们以不同方式连接而型成网络。人工神经网络就是尝试模拟这种生物学上的体系结构及其操作。在这里有一个难题：我们对生物学上的神经网络知道的不多！因此，不同类型之间的神经网络体系结构有很大的不同，我们所知道的只是神经元基本的结构。

The neuron 
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虽然已经确认在我们的大脑中有大约50至500种不同的神经元，但它们大部份都是基于基本神经元的特别细胞。基本神经元包含有synapses、soma、axon及dendrites。Synapses负责神经元之间的连接，它们不是直接物理上连接的，而是它们之间有一个很小的空隙允许电子讯号从一个神经元跳到另一个神经元。然后这些电子讯号会交给soma处理及以其内部电子讯号将处理结果传递给axon。而axon会将这些讯号分发给dendrites。最后，dendrites带着这些讯号再交给其它的synapses，再继续下一个循环。

如同生物学上的基本神经元，人工的神经网络也有基本的神经元。每个神经元有特定数量的输入，也会为每个神经元设定权重（weight）。权重是对所输入的资料的重要性的一个指标。然后，神经元会计算出权重合计值（net value），而权重合计值就是将所有输入乘以它们的权重的合计。每个神经元都有它们各自的临界值（threshold），而当权重合计值大于临界值时，神经元会输出1。相反，则输出0。最后，输出会被传送给与该神经元连接的其它神经元继续剩余的计算。

Learning 
----------------------------------------------------------------------

正如上述所写，问题的核心是权重及临界值是该如何设定的呢？世界上有很多不同的训练方式，就如网络类型一样多。但有些比较出名的包括back-propagation, delta rule及Kohonen训练模式。

由于结构体系的不同，训练的规则也不相同，但大部份的规则可以被分为二大类别 - 监管的及非监管的。监管方式的训练规则需要“教师”告诉他们特定的输入应该作出怎样的输出。然后训练规则会调整所有需要的权重值（这是网络中是非常复杂的），而整个过程会重头开始直至数据可以被网络正确的分析出来。监管方式的训练模式包括有back-propagation及delta rule。非监管方式的规则无需教师，因为他们所产生的输出会被进一步评估。

Architecture 
----------------------------------------------------------------------

在神经网络中，遵守明确的规则一词是最“模糊不清”的。因为有太多不同种类的网络，由简单的布尔网络（Perceptrons），至复杂的自我调整网络（Kohonen），至热动态性网络模型（Boltzmann machines）！而这些，都遵守一个网络体系结构的标准。

一个网络包括有多个神经元“层”，输入层、隐蔽层及输出层。输入层负责接收输入及分发到隐蔽层（因为用户看不见这些层，所以见做隐蔽层）。这些隐蔽层负责所需的计算及输出结果给输出层，而用户则可以看到最终结果。现在，为免混淆，不会在这里更深入的探讨体系结构这一话题。对于不同神经网络的更多详细资料可以看Generation5 essays

尽管我们讨论过神经元、训练及体系结构，但我们还不清楚神经网络实际做些什么。

The Function of ANNs 
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神经网络被设计为与图案一起工作 - 它们可以被分为分类式或联想式。分类式网络可以接受一组数，然后将其分类。例如ONR程序接受一个数字的影象而输出这个数字。或者PPDA32程序接受一个坐标而将它分类成A类或B类（类别是由所提供的训练决定的）。更多实际用途可以看Applications in the Military中的军事雷达，该雷达可以分别出车辆或树。

联想模式接受一组数而输出另一组。例如HIR程序接受一个‘脏’图像而输出一个它所学过而最接近的一个图像。联想模式更可应用于复杂的应用程序，如签名、面部、指纹识别等。

The Ups and Downs of Neural Networks  
----------------------------------------------------------------------

神经网络在这个领域中有很多优点，使得它越来越流行。它在类型分类/识别方面非常出色。神经网络可以处理例外及不正常的输入数据，这对于很多系统都很重要（例如雷达及声波定位系统）。很多神经网络都是模仿生物神经网络的，即是他们仿照大脑的运作方式工作。神经网络也得助于神经系统科学的发展，使它可以像人类一样准确地辨别物件而有电脑的速度！前途是光明的，但现在...

是的，神经网络也有些不好的地方。这通常都是因为缺乏足够强大的硬件。神经网络的力量源自于以并行方式处理资讯，即是同时处理多项数据。因此，要一个串行的机器模拟并行处理是非常耗时的。

神经网络的另一个问题是对某一个问题构建网络所定义的条件不足 - 有太多因素需要考虑：训练的算法、体系结构、每层的神经元个数、有多少层、数据的表现等，还有其它更多因素。因此，随着时间越来越重要，大部份公司不可能负担重复的开发神经网络去有效地解决问题。

NN 神经网络，Neural Network 
ANNs 人工神经网络，Artificial Neural Networks 
neurons 神经元 
synapses 神经键 
self-organizing networks 自我调整网络 
networks modelling thermodynamic properties 热动态性网络模型 





英文翻译
Introduction 
----------------------------------------------------------------------
     Neural network is a new technology in the field of fashion vocabulary. Many people have heard of the word, but few people really understand what it is. The purpose of this paper is to introduce all the basic neural network functions, including its general structure, related terms, types and applications.


   "Neural network" actually came from biology, and neural networks we refer the correct name should be "Artificial Neural Networks (ANNs)". In this article, I will also use the two interchangeable terms.
    A real neural network is a few to a few billion cells called neurons (composed of tiny cells in our brains) are composed of, they are different ways to connect and type into the network. Artificial neural network is trying to model this biological system structure and its operation. There is a problem here: we biological neural networks do not know much! Thus, between different types of neural network architecture is very different, we know only the basic structure of neurons.
    The neuron 
----------------------------------------------------------------------
    While already recognized in our brain, about 50 to 500 kinds of different neurons, but most of them are based on special cells in the basic neuron. Contains the basic neural synapses, soma, axon and dendrites. Synapses between neurons responsible for the connection, they are not directly physically connected, but they have a very small gap between to allow electronic signals from one neuron to another neuron. Then the electrical signals to the soma will be an internal electronic signal processing and its processing result will pass axon. The axon of these signals will be distributed to dendrites. Finally, dendrites with these signals and then to the other synapses, and then continue to the next cycle.
    As a basic biological neurons, artificial neural networks have basic neurons. Each neuron has a specific number of inputs, will be set for each neuron weight (weight). Weight is the importance of the information entered an indicator. Then, neurons calculates the weight of the total value (net value), while the total weight of all the input value is multiplied by the total of their weights. Each neuron has their own threshold (threshold), while the power is greater than the critical value of the total value of weight, the neuron will output 1. On the contrary, the output 0. Finally, the output can be transmitted to the neuronal connections with other neurons to the remaining calculations.
     Learning 
----------------------------------------------------------------------     As written above, at issue is the critical value of the weight and how to set it? The world has many different training methods, as much as the network type. But some well-known, including back-propagation, delta rule and Kohonen training mode.
      Because of different structural systems, training is not the same rules, but most of the rules can be divided into two broad categories - regulatory and non-regulated. Supervising the training rules need to be "teachers" tell them how a particular input to the output should be. Then the training rule to adjust the weight of all the needs of value (this is a very complex network), and the whole process would start again until the correct data can be analyzed by the network. Regulatory approach of the training model includes back-propagation and the delta rule. The rules of non-regulatory approach without teachers, because they produce the output will be further evaluated.
    Architecture 
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    In the neural network, comply with the rules clear word is the most "obscure" the. Because there are too many different types of networks, from simple Boolean networks (Perceptrons), to the complex network of self-adjustment (Kohonen), to the thermal dynamic network model (Boltzmann machines)! These have to comply with the standards of a network architecture.
    A network including multiple neurons, "layer", the input layer, hidden layer and output layer. Input layer to receive input and distribute to the hidden layer (because the user can not see the layers, so do see the hidden layer). The hidden layer is responsible for the necessary calculations and output to the output layer, the user can see the final result. Now, to avoid confusion, would not be here more in-depth study architecture talking about it. Different neural networks for more detailed information can be read Generation5 essays, including a multiple neural network "layer", the input layer, hidden layer and output layer. Input layer to receive input and distribute to the hidden layer (because the user can not see the layers, so do see the hidden layer). The hidden layer is responsible for the necessary calculations and output to the output layer, the user can see the final result. Now, to avoid confusion, would not be here more in-depth study architecture talking about it. Different neural networks for more detailed information can be seen Generation5 essays.
    Although we discussed the neurons, training and architecture, but we do not know what the actual neural network.
    The Function of ANNs 
----------------------------------------------------------------------    Neural networks are designed to work with patterns - they can be divided into two categories-type or association type. Category-type network can accept a few, and then classified. For example, ONR program accepts a number of the image and the output figure. Or PPDA32 program accepts a coordinate and to classify it as Class A or B (type of training provided by the decision). More practical use can be seen Applications in the Military in the military radars, the radar could pick out a vehicle or tree.
    Lenovo model to accept a group of numbers and the output of another group. HIR procedures such as acceptance of a 'dirty' image and the output of a learned and the closest it an image. Lenovo model also can be used in complex applications such as signature, face, fingerprint recognition.
    The Ups and Downs of Neural Networks  
----------------------------------------------------------------------    Neural network in this area has many advantages, making it more popular. It is in the type classification / recognition is very good. Neural networks can handle the exception and not the normal input data, which are important for many systems (such as radar and sonar systems). Many neural networks are mimic biological neural networks, that is their mode of operation modeled on the work of the brain. Neural networks also have to help the development of neuroscience, it can, like humans, accurate identification of objects and the speed of computers! The future is bright, but now ...
    Yes, the neural network are also some bad points. This is usually because of lack of sufficiently powerful hardware. Power derived from the neural network to process information in parallel, that is, a number of data simultaneously. Therefore, to simulate a serial parallel processing machines is very time-consuming.
    Another problem with neural networks is a problem in building a network of defined conditions are not - there are too many factors to consider: training algorithms, architecture, number of neurons in each layer, the number of layers, data show, etc. There are other additional factors. Therefore, more and more important over time, most companies can not afford to repeat the development of neural network to effectively solve the problem.
   


不知道是不是  我随便找的

7. 我正在准备一篇文章 要分析若干因子对其影响的大小 要用到BP神经网络模型分析 要如何用matlab实现。。。。

我也是一个初学者，bp神经网络的学习可以参考以下程序。不对的还请见谅，
 
 
% NEWFF——生成一个新的前向神经网络 
% TRAIN——对 BP 神经网络进行训练 
% SIM——对 BP 神经网络进行仿真 

 
%  定义训练样本 
% P 为输入矢量 
P=pixel_value'
% T 为目标矢量 
T=t
size(P)
size(T)
% size(P)
% size(T)

 
%  创建一个新的前向神经网络  
net_1=newff(minmax(P),[10,3],{'tansig','purelin'},'traingdm')

 
%  当前输入层权值和阈值 
inputWeights=net_1.IW{1,1} 
inputbias=net_1.b{1} 
%  当前网络层权值和阈值 
layerWeights=net_1.LW{2,1} 
layerbias=net_1.b{2} 

 
%  设置训练参数
net_1.trainParam.show = 50; 
net_1.trainParam.lr = 0.05; 
net_1.trainParam.mc = 0.9; 
net_1.trainParam.epochs = 10000; 
net_1.trainParam.goal = 1e-3; 

 
%  调用 TRAINGDM 算法训练 BP 网络[net_1,tr]=train(net_1,P,T); 
%  对 BP 网络进行仿真
A = sim(net_1,P); 
%  计算仿真误差  
E = T - A; 
MSE=mse(E) 

 
x=[0.14 0 1 1 0 1 1 1.2]';sim(net_1,x)

8. 求助一篇 关于神经网络（通信方面）有关的英文论文，谢谢

只要作者名字吗·连个邮箱都不留 
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