神經網路英語怎麼說及英文翻譯

㈠ 高分求神經網路方面的英文論文和中文翻譯

我發了 看看怎麼樣

㈡ 神經網路的英文翻譯

neural net
神經網路；〔神經網路〕
A unique function of memory and information processing necessary for a biological type of computer memory. It includes (a) random organization; (b) the distribution of memory traces through the entire system; (c) the simultaneous participation of any element in many memory traces; (d) no catastrophic failure; (e) implicit or response reinforcement memory; and (f) automatic response (no search and comparison would be needed).
一種獨特的存儲和信息處理功能，是生物型計算機存儲器所必須具備的，其中包括：(1)隨機組織；(2)存儲跟蹤分布遍及整個系統；(3)許多存儲跟蹤中任何元素的同時加入；(4)不出現突然失效；(5)隱含或反應增強式存儲；(6)自動反應(不需要搜索與比較)。

㈢ 人工神經網路英文怎麼寫

人工神經網路

英文翻譯如下：

artificial neural network

雙語例句：

1、This paper discussed the application of artificial neural network at the resistance spot welding of aluminum alloy.

針對鋁合金的電阻點焊，研究了人工神經網路在點焊質量估測中的應用。

2、In the paper, data preparative and Artificial Neural Network parameter in DM is discussed mainly.

在論文中，著重考慮了人工神經網在數據挖掘中的數據准備問題、網路參數的選取問題等等。

3、Artificial neural network and its application in near infrared spectros are presented in brief.

簡要介紹了人工神經網路及其在近紅外光譜分析中的應用。

㈣ 求一篇關於神經網路的英文翻譯

Introction
--------------------------------------------------------------------

「神經網路」這個詞實際是來自於生物學，而我們所指的神經網路正確的名稱應該是「人工神經網路（ANNs）」。在本文，我會同時使用這兩個互換的術語。

一個真正的神經網路是由數個至數十億個被稱為神經元的細胞（組成我們大腦的微小細胞）所組成，它們以不同方式連接而型成網路。人工神經網路就是嘗試模擬這種生物學上的體系結構及其操作。在這里有一個難題：我們對生物學上的神經網路知道的不多！因此，不同類型之間的神經網路體系結構有很大的不同，我們所知道的只是神經元基本的結構。

The neuron
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雖然已經確認在我們的大腦中有大約50至500種不同的神經元，但它們大部份都是基於基本神經元的特別細胞。基本神經元包含有synapses、soma、axon及dendrites。Synapses負責神經元之間的連接，它們不是直接物理上連接的，而是它們之間有一個很小的空隙允許電子訊號從一個神經元跳到另一個神經元。然後這些電子訊號會交給soma處理及以其內部電子訊號將處理結果傳遞給axon。而axon會將這些訊號分發給dendrites。最後，dendrites帶著這些訊號再交給其它的synapses，再繼續下一個循環。

如同生物學上的基本神經元，人工的神經網路也有基本的神經元。每個神經元有特定數量的輸入，也會為每個神經元設定權重（weight）。權重是對所輸入的資料的重要性的一個指標。然後，神經元會計算出權重合計值（net value），而權重合計值就是將所有輸入乘以它們的權重的合計。每個神經元都有它們各自的臨界值（threshold），而當權重合計值大於臨界值時，神經元會輸出1。相反，則輸出0。最後，輸出會被傳送給與該神經元連接的其它神經元繼續剩餘的計算。

Learning
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正如上述所寫，問題的核心是權重及臨界值是該如何設定的呢？世界上有很多不同的訓練方式，就如網路類型一樣多。但有些比較出名的包括back-propagation, delta rule及Kohonen訓練模式。

由於結構體系的不同，訓練的規則也不相同，但大部份的規則可以被分為二大類別 - 監管的及非監管的。監管方式的訓練規則需要「教師」告訴他們特定的輸入應該作出怎樣的輸出。然後訓練規則會調整所有需要的權重值（這是網路中是非常復雜的），而整個過程會重頭開始直至數據可以被網路正確的分析出來。監管方式的訓練模式包括有back-propagation及delta rule。非監管方式的規則無需教師，因為他們所產生的輸出會被進一步評估。

Architecture
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在神經網路中，遵守明確的規則一詞是最「模糊不清」的。因為有太多不同種類的網路，由簡單的布爾網路（Perceptrons），至復雜的自我調整網路（Kohonen），至熱動態性網路模型（Boltzmann machines）！而這些，都遵守一個網路體系結構的標准。

一個網路包括有多個神經元「層」，輸入層、隱蔽層及輸出層。輸入層負責接收輸入及分發到隱蔽層（因為用戶看不見這些層，所以見做隱蔽層）。這些隱蔽層負責所需的計算及輸出結果給輸出層，而用戶則可以看到最終結果。現在，為免混淆，不會在這里更深入的探討體系結構這一話題。對於不同神經網路的更多詳細資料可以看Generation5 essays

盡管我們討論過神經元、訓練及體系結構，但我們還不清楚神經網路實際做些什麼。

The Function of ANNs
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神經網路被設計為與圖案一起工作 - 它們可以被分為分類式或聯想式。分類式網路可以接受一組數，然後將其分類。例如ONR程序接受一個數字的影象而輸出這個數字。或者PPDA32程序接受一個坐標而將它分類成A類或B類（類別是由所提供的訓練決定的）。更多實際用途可以看Applications in the Military中的軍事雷達，該雷達可以分別出車輛或樹。

聯想模式接受一組數而輸出另一組。例如HIR程序接受一個『臟』圖像而輸出一個它所學過而最接近的一個圖像。聯想模式更可應用於復雜的應用程序，如簽名、面部、指紋識別等。

The Ups and Downs of Neural Networks
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神經網路在這個領域中有很多優點，使得它越來越流行。它在類型分類/識別方面非常出色。神經網路可以處理例外及不正常的輸入數據，這對於很多系統都很重要（例如雷達及聲波定位系統）。很多神經網路都是模仿生物神經網路的，即是他們仿照大腦的運作方式工作。神經網路也得助於神經系統科學的發展，使它可以像人類一樣准確地辨別物件而有電腦的速度！前途是光明的，但現在...

是的，神經網路也有些不好的地方。這通常都是因為缺乏足夠強大的硬體。神經網路的力量源自於以並行方式處理資訊，即是同時處理多項數據。因此，要一個串列的機器模擬並行處理是非常耗時的。

神經網路的另一個問題是對某一個問題構建網路所定義的條件不足 - 有太多因素需要考慮：訓練的演算法、體系結構、每層的神經元個數、有多少層、數據的表現等，還有其它更多因素。因此，隨著時間越來越重要，大部份公司不可能負擔重復的開發神經網路去有效地解決問題。

NN 神經網路，Neural Network
ANNs 人工神經網路，Artificial Neural Networks
neurons 神經元
synapses 神經鍵
self-organizing networks 自我調整網路
networks modelling thermodynamic properties 熱動態性網路模型

英文翻譯
Introction
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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 introce 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
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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 proce 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 proceres 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 我隨便找的

㈤ 求翻譯兩段關於神經網路的英文（我已經用翻譯軟體翻譯了一下）

我還是重新幫你譯了一遍，希望你能看懂文章

In one of the largest applications of neural network to data, Le Cun et al.(1989) have implemented a network designed to read zip codes on hand-addressed envelopes. The system uses a preprocessor that locates and segments the indivial digits in the zipcode; the network has to identify the digits themselves. It uses a 16×16 array of pixels as input, three hidden layers, and a distributed output encoding with 10 output units for digits 0-9. The hidden layers contained 768,192, and 30 units, respectively. A fully connected network of this size would contain 200,000 weights, and would be impossible to train. Instead, the network was designed with connections intended to act as feature detectors. For example, each unit in the first hidden layer was connected by 25 links to a 5×5 region in the input. Furthermore, the hidden layer was divided into 12 groups of 64 units, each unit used the same set of 25 weights. Hence the hidden layer can detect up to 12 distinct features, each of which can occur anywhere in the input image. Overall, the complete network used only 9760 weights.
【神經網路在數據中的一個最大應用中，Le Cun等人在1989年提出了一種從手寫信封讀取郵編的網路設計方法。這個系統用一個處理器來定位並劃分郵編中的各位數字。網路用來識別各個數字。它使用16像素×16像素的陣列作為輸入，包括3個隱層，以及一個分布式輸出來解碼並與10個代表數字0-9的輸出單元相匹配。3個隱層分別包含768個，192個和30個單元。一個完整鏈接的網路大約有20萬的權重，但是不可以再進行學習。因此取而代之的是，網路中設計了一些可以用作特徵檢測的鏈接。比如，第一個隱層中的每個單元被通過25個鏈接連到輸入中一個5×5的區域。此外，隱層還被區分成12組每組64單元，每個單元使用同樣配置的25個權重。因此隱層一共能夠檢測12種不同的特徵，而且這些特徵可能存在於輸入圖像的任意區域中。總的來說，整個網路只需要用到9760個權重。】
et al.是論文中多作者時表示省略的用法

The network was trained on 7300 examples, and tested on 2000. One interesting property of a network with distributed output encoding is that it can display confusion over the correct answer by setting two or more output units to a high value. After rejecting about 12% of the test set as marginal, using a confusion threshold, the performance on the remaining cases reached 99%, which was deemed adequate for an automated mail-sorting system. The final network has been implemented in custom VLSI, enabling letters to be sorted at high speed.
【(設計的)網路通過了7300多個實例的學習，並且在2000個實例中進行了驗證。具有分布式輸出解碼的網路的一個有趣的性質是，它能夠通過將2個或多個單元設置為「高」來顯示對識別正確結果的「困惑」（其實個人理解這里就是一個值，這個值到了一定水平就表示識別不出來了）。在設置12%誤判率作為測試成敗的分界點後，使用一個「困惑閾值」，省下的實例的辨別率高達99%，這在郵件自動分揀系統中已經切實達到要求了。最終的網路被通過定製好的VLSI(超大規模集成電路)實現，並且能完成郵件的高速分揀。】

希望回答對你有幫助

㈥ 英語Neural Engine怎麼翻譯

英語Neural Engine翻譯成中文是：「神經引擎」。

重點詞彙：neural

一、單詞音標

• neural單詞發音：英[ˈnjʊərəl]美[ˈnʊrəl]。
  

二、單詞釋義

• adj.神經的

三、詞形變化

• 副詞擴展:neurally

四、短語搭配

• neural network神經網路

• neural arch神經弓

• neural control神經控制

• neural plate神經板

五、雙語例句

• 神經網路是模擬大腦工作方式的計算機系統。

• .

  它每秒可以創建多達一百萬個神經連接。

• .

  信息傳遞的神經途徑有賴於神經遞質。

• isthatitreallyenhanceswhat'scalledneuralintegration.

  它可以增強神經整合

• ,thatonesynapse.

  你擁有一處單一的神經連接，那一個特定突觸。

㈦ 急求！人工神經網路中英文翻譯！

GOOGLE 公司出資有一個人工翻譯團隊免費提供服務，你只要點一下他們下面的一個廣告鏈接表示感恩。網站：www.gramlee.com

你先用機器翻譯一下。

在GOOGLE上翻譯一次，要不用www.nicetranslator.com 先按照一個小段一段段的翻譯好，也可以。然後發過去，他們會人工幫你翻譯，不過可能要等幾天才發回來。

這個是我們做外貿，外貿老師培訓介紹的。NICE TRANSLATOR還可以，那個人工翻譯網站我沒試驗過。
希望能幫到你

㈧ 神經網路用英語怎麼說

shen jing wang luo

㈨ 神經網路分類器翻譯成英文怎麼說

neural network classifiers

http://www.lunwenjia.com/zidonghuajc/388002.html

㈩ 這段英文怎麼翻譯，關於神經網路的英文文獻摘要

專業詞彙很多，如果可以的話，建議自己根據網路翻譯的結果，自己調整一下語序，或者找個相關專業的給你翻譯一下，用翻譯軟體直接翻譯的是不行的翻，附網路翻譯結果：
文章考慮了時變離散和分布時變時滯的細胞神經網路時滯相關魯棒穩定性。基於Lyapunov穩定性理論和線性矩陣不等式（LMI）技術，時滯相關的穩定性判據推導出的線性矩陣不等式避免邊界某些交叉項，這往往導致停滯。在數值例子中示出的建議的穩定性判據和改進現有的結果的有效性。
尤其是當中的基於Lyapunov穩定性理論和線性矩陣不等式（LMI）技術，時滯相關的穩定性判據推導出的線性矩陣不等式避免邊界某些交叉項，這往往導致停滯。