【正文】 one nust decide how to encode the examples in terms of inputs and outputs of the network. An ANN is a network of many very simple processors(units),each possibly having a (small amount of) local memory. The units are connected by unidirectional munication channels(“connections”),which carry numeric(as opposed to symbolic) data. The units operates only on their local data and on the inputs they receive via the connections. The design motivation is what distinguishes neural networks form other mathematical techniques: A neural network is a processing device, either an algorithm, or actual hardware, Whose design was motivated by the design and functioning of human brains and ponents of thereof. Most neural networks have some sort of “training” rule whereby the weights of connections are adjusted on the basis of presented patterns. In other words ,neural networks “l(fā)earn” form examples, just like children learn to recognize dogs from examples of dogs, and exhibit some structural capability for generalization.Neural networks normally have great potential for parallelism, since the putations of the ponents are independent of each other.In principle NNS can puter any putable function, . they can do everything a normal digital puter can do. Especially anything that can be represented as a mapping between vector spaces can be approximated to arbitrary precision by fed forward NNS (which is the often used type).In practice, NNs are especially useful for mapping problems, which are tolerant of some errors, have lots of example data available, but to which hard and fast rules cannot easily be applied. NNs are, at least today, difficult to problems that concern manipulation of symbols and memory.Neural Networks are interesting for quite a lot of very dissimilar people: puter scientists want to find out about the properties nonsymbolic information processing with neural nets and about learning systems in general.Engineers of many kinds want to exploit the capabilities of neural networks on many areas(. signal processing)to solve their application problems.Cognitive scientists view neural networks as a possible apparatus to describe models of thinking and conscience (Highlevel brain function).Suppose we want to construct a network for the restaurant problem. We have already seen that a perceptronis inadequate, so we will try a twolayer network. We have ten attributes describing each example, so we will need ten input units. Learning in such a network proceeds the same way as for perceptrons: example inputs are presented to the network, and if the network putes an output vector that matches the target, nothing is done. If there is an error (a difference between the output and target), then the weights are adjustec to reduce this error. The trick is to assess the blame for an error and divide it among the contributing weights. In perceptrons, this is easy because there is only one weight between each input and the output. But in multilayer networks. There are many weights connecting each input to an output, and each of these weights contributes to more than one output. The backpropagation algorithm is a sensibly approach to dividing the contribution of each weight. As in the perceptron learning algorithm, we try to minimize the error between each target output and the output actually puted by the network. At the output layer, the weight update rule is very similar to the rule for the perceptron. There are two differences: the activation of the hidden unit aj is used instead of the input value。 }}/*******************************************************************************名稱：time0()*功能： 中斷服務(wù)，對(duì)熒電壓進(jìn)行采樣，存儲(chǔ)*入口參數(shù)：無*出口參數(shù)：無******************************************************************************/void time0(uint interval){ uchar th,tl。 T2CON=0x00。 P2 = 0xef。 //傳感器返回值除16得實(shí)際溫度值 //為了得到2位小數(shù)位，先乘100，再除16，考慮整型數(shù)據(jù)長(zhǎng)度， //技巧處理后先乘25，再除4，除4用右移實(shí)現(xiàn) t = (b*256+a)*25。0x01。 //稍做延時(shí)后 如果x=0則初始化成功 x=1則初始化失敗 delay_18B20(20)。與我一同工作的同志對(duì)本研究所做的任何貢獻(xiàn)均已在論文中作了明確的說明并表示了致謝。由于熱電偶具有準(zhǔn)確度高、測(cè)溫范圍廣和成本低廉等優(yōu)點(diǎn)，在工業(yè)應(yīng)用中仍是溫度測(cè)量的首選。： 顯示刷新子程序 按鍵掃描處理子程序按鍵采用掃描查詢方式，設(shè)置標(biāo)志位，當(dāng)標(biāo)志位為1時(shí)，顯示設(shè)置溫度，否則顯示溫度。5)LIB51庫(kù)管理器:從目標(biāo)模塊生成連接器可以使用的庫(kù)文件。K3的功能是：設(shè)定報(bào)警溫度的減功能。在上述著名的半導(dǎo)體企業(yè)產(chǎn)品:，扣，尤其在工業(yè)測(cè)控場(chǎng)合，運(yùn)用較多的為Intel公司的MCs一51系列和Mieroehip公司的PIC系列單片機(jī)。 功能簡(jiǎn)介（1） 適應(yīng)電壓范圍更寬，電壓范圍： V～，在寄生電源方式下可由數(shù)據(jù)線供電。ADS7804 采用CMOS 工藝制造，轉(zhuǎn)換速度快、功耗低（最大功耗為100mW）。廣泛應(yīng)用于穩(wěn)定積分、比較器、密絕對(duì)值電路、及微弱信號(hào)的精確放大等場(chǎng)合。其實(shí)質(zhì)是一個(gè)可產(chǎn)生直流信號(hào)為風(fēng)。參考端的溫度變化將影響溫度測(cè)量的準(zhǔn)確度。信號(hào)經(jīng)過調(diào)理之后，需要將模擬信號(hào)轉(zhuǎn)換為數(shù)字信號(hào)，在本系統(tǒng)中選擇ADS7804作為A/D轉(zhuǎn)換器。目前，對(duì)于低至ZK(零下271℃)而高達(dá)2800℃甚至更高的溫度測(cè)量，金屬熱電偶都可以勝任。熱電阻在溫度變化的時(shí)候其自身電阻也隨著發(fā)生變化，其感溫元件是采用細(xì)金屬絲均勻地雙繞在絕緣材料制成的骨架上，當(dāng)被測(cè)量介質(zhì)中有溫度存在時(shí)，所測(cè)得的溫度是感溫元件所在介層中的平均溫度。然而對(duì)于旋轉(zhuǎn)或移動(dòng)的物體溫度進(jìn)行檢測(cè)時(shí)，則遇到很大障礙，因此采用無線傳輸方式的測(cè)溫技術(shù)就具有非常大的優(yōu)勢(shì)。(3)用輻射溫度計(jì)或熱像儀測(cè)量表面溫度分布。隨著國(guó)內(nèi)外工業(yè)的日益發(fā)展，溫度測(cè)量技術(shù)也有了長(zhǎng)足地進(jìn)步。兩點(diǎn)間作100等分，每一份稱為1攝氏度。該論文主要由智能溫度測(cè)量?jī)x表的硬件設(shè)計(jì)、軟件設(shè)計(jì)兩個(gè)部分組成。Anders Celsius(17011744)，其結(jié)冰點(diǎn)是0176。在實(shí)際使用中，因?yàn)槭艿礁鞣N因素的影響，熱電偶的溫度和熱電勢(shì)之間呈非線性關(guān)系。(2)光纖式溫度分布測(cè)量裝置。因此，在加熱物體的表面，如能消除此溫度梯度，就能測(cè)量出便可知內(nèi)部溫度。第2章 系統(tǒng)總體設(shè)計(jì)第2章 系統(tǒng)總體設(shè)計(jì) 典型測(cè)溫系統(tǒng)1. 熱電阻測(cè)溫系統(tǒng)熱電阻測(cè)溫系統(tǒng)是基于金屬導(dǎo)體的電阻值隨著溫度的增加而增大這一特性來進(jìn)行溫度測(cè)量的。2)結(jié)構(gòu)簡(jiǎn)單，便于維修原則上，只要將兩種不同的導(dǎo)體或半導(dǎo)體的一端焊接在一起并對(duì)其他部分加以絕緣保護(hù)，便可組成一支完整的熱電偶。選擇數(shù)字溫度傳感器DS1SBZO進(jìn)行冷端溫度的測(cè)量以進(jìn)行補(bǔ)償。K型熱電偶的負(fù)極受磁場(chǎng)影響明顯，在使用時(shí)應(yīng)盡量減少周圍場(chǎng)對(duì)熱電偶的干擾。電子子式冰點(diǎn)裝置優(yōu)點(diǎn)是體積小、操作簡(jiǎn)單。由于采用絕對(duì)值調(diào)整技術(shù)，所以只須用一只電阻即可精確地設(shè)置增益(當(dāng)G=100時(shí)，%)。ADS7804作為A/D轉(zhuǎn)換器。: 數(shù)字溫度傳感器DS18B20由第2章可知，熱電偶作為目前溫度測(cè)量中使用最普遍的傳感器之一，其輸般將熱電偶參比溫度端(冷端)保持為O℃，并且各種型號(hào)熱電偶的分度表所列數(shù)值都是以參比端溫度為0℃為基準(zhǔn)而制成的，但在實(shí)際中做到這一點(diǎn)很困難，于是產(chǎn)生了熱電偶冷端補(bǔ)償問題。它把中央處理器、存儲(chǔ)器、輸入/輸出接口電路以及定時(shí)器/計(jì)數(shù)器集成在一塊芯片上，從而具有體積小、功耗低、價(jià)格低廉、抗千擾能力強(qiáng)且可靠性高等特點(diǎn)，因此適合應(yīng)用于工業(yè)過程控制、智能儀器儀表和測(cè)控系統(tǒng)的前端裝置。掉電方式保存 RAM中的內(nèi)容，但振蕩器停止工作并禁止其它所有部件工作直到下一個(gè)硬件復(fù)位。其內(nèi)嵌多種符合當(dāng)前工業(yè)標(biāo)準(zhǔn)的開發(fā)工具，可以完成從工程建立到管理、編譯、鏈接、目標(biāo)代碼的生成、軟件仿真、硬件仿真等完整的開發(fā)流程，尤其是C編譯工具在產(chǎn)生代碼的準(zhǔn)確性和效率方面達(dá)到了較高的水平，而且可以附加靈活的控制選項(xiàng)，在開發(fā)大型項(xiàng)目時(shí)非常理想。如果計(jì)算結(jié)果是0度以下，則置“0”，若是0度以上，則置“1”。本論文的研究實(shí)驗(yàn)過程中，主要進(jìn)行了一下工作：1. 通過對(duì)K型熱電偶測(cè)溫原理以及熱電偶的溫度補(bǔ)償方法的分析，設(shè)計(jì)了采用數(shù)字溫度傳感器DS18B20來對(duì)其進(jìn)行冷端補(bǔ)償和信號(hào)調(diào)理的方法。畢業(yè)設(shè)計(jì)（論文）知識(shí)產(chǎn)權(quán)聲明畢業(yè)設(shè)計(jì)（論文）知識(shí)產(chǎn)權(quán)聲明本人完全了解西安工業(yè)大學(xué)有