隱含層 的英文怎麼說
中文拼音 [yǐnháncéng]
隱含層
英文
hidden layer-
In viewing narrative as rhetoric, phelan emphasizes narrative as a multilayered communication from an implied author to an authorial audience by means of textual strategies
費倫將敘事看成修辭，置重於隱含讀者通過文本策略與作者的讀者進行的多層次交流。Net framework types use a dot syntax naming scheme that connotes a hierarchy
. net framework類型使用點語法命名方案，該方案隱含了層次結構的意思。Firstly, second harmonic component ratio and dead angles of two phase inrush ' s dispersion in three - phase transformes are acted as input variable. secondly, the method applies improved algorithm based on the original algorithm of multi - layer forward back propagation network, that is to say, adding last variational effect of weight value and bias value to this time and making use of variable learning rate. at the same time, this method also adopts dynamic form in the number of hidden floor node
首先，文中將三相變壓器兩相涌流差流的二次諧波含量比和間斷角作為網路的輸入變量；其次，利用對原有bp網路訓練演算法基礎上的改進型演算法（即在計算本次權值和閾值的變化時增加上一次權值和閾值變化的影響以及採用變學習率，與此同時隱含層神經元個數採用動態形式） ，通過樣本訓練使網路結構模型達到最優。However geological information is fully recorded in the remote sensing image, which made it possible for the choosing of this area as a dissection point to extract complex structural information of orogenic belt in west china. taking fully advantage of multi - band image richly bearing concealed geological information in combination with remote sensing analysis and structure analysis, to anatomy the supracrustal composition and structure of orogenic belt with the regional linear structures and their partitioned block and schistous geological masses as the macro - frame ( in corresponding to structure units and structure segments ) and with the rock masses, structure - rock assemblages, line - featured and belt - featured structures as well as penetrative and non - penetrative foliation ( primary stratum and trans position layering ) and folds as the texture and structure elements. the methods of how to distinguish granulite > ductile - shear zone, imposed fold, different deformed belts -
因而，本文選擇這一地區作為我國西部地區從遙感圖像上提取造山帶復雜結構構造信息的解剖區，充分利用遙感圖像多波段反映物質屬性的特點和圖像處理提取隱含信息的優勢，採用遙感解析?構造解析相結合的研究方法，以區域線狀構造及由它劃分的塊狀、片狀地質體為宏觀骨架（對應于構造解析劃分的構造單元、構造均勻區段） ，以地質體中的巖石巖體、構造巖石組合，線狀、帶狀構造，透入性、非透入性面狀（原始層理、新生面理）和褶皺等構造作為用於解析的結構構造要素，進行造山帶表殼組成和結構構造解析研究。In order to improve generalization capability of feedforward neural networks, the convinced networks generalization domain should be guaranteed to be close to networks input domain as maximal intrinsic error of networks output and maximal samples error are reduced by increasing hidden neurons in number in the progress of networks learning, otherwise generalization capability of feedforward neural networks is likely to be decreased
為了提高網路的泛化性能，從理論上分析指出，在網路學習過程中通過增加隱含層神經元來降低網路最大固有誤差和最大樣本誤差的同時，要求確保網路泛化定義域盡可能接近網路輸入定義域，否則將有可能降低網路的泛化性能。A 3 layer - neural network is used as classifier, the number of nodes in hide layer is determined on trial and error
用前向三層神經網路作為蘋果特徵的分類器，結合試驗結果，用試湊法確定了隱含層節點的個數。Based on the aerodynamics, control, structural dynamics model of smart rotor in frequency domain deduced and the determination for the number of neurons in hidden layer, the neuro - emulator using multiple independent miso neural networks with its deduced matrix expression for the smart rotor is set up. the rate of training is improved by introducing the orthogonal selection applying for smart rotor to the selection of training cases in neural modeling
試驗結果驗證了該方法的可行性，在建立了帶有主動控制后緣附翼的智能旋翼系統氣動-控制-結構動力學數學模型的基礎上，提出了適用於智能旋翼建模的多神經網路並聯型式的頻域模型，並推導出其矩陣表達式，探討了隱含層神經元數的確定方法。The network consists of three layers : the input layer, the hidden layer, and the output layer
文中的bp網路模型都是由三層構成：輸入層、隱含層、輸出層。The network has four layers. input layer has 16 nodes. the first hidden layer has 17 nodes ; the second hidden layer has 10 nodes and one output node. 37 projects " data is used in training samples
網路共有四層，輸入層節點數為16個，隱含層一的節點數為17個，隱含層二的節點數10為個，輸出層節點1個。Aiming at the problem as dormant layer points, a new method named gdann was studied. with the method, the best ann model can be acquired
針對神經網路模型中存在的隱含層結點數難以確定等問題，研究了灰色動態神經網路模型。A new way used to decide the neuron ' s number of the hidden layer is proposed based on the analysis and on the experiential way proposed by others
對神經網路隱含層的作用進行分析，在此基礎上，借鑒有關文獻提出的經驗公式，提出了確定隱含層節點數的新方法。In this kind of networks, rough neurons locate in the hidden - layer and they consist of three parts which generated by two hyperplanes partition universe. the hyperplanes are obtained by support vector machines
該方法引入多個類似於支持向量機的子神經網路，並將網路中的隱含層單元設計成由多組粗糙神經元構成的網路單元。To conclude from the given examples, each model, after spending little time on training themselves from sample data, could assess the damage degree for existing bridges using trained weighted values and thresholds
實例計算表明，各網路模型花費很少的時間完成對樣本的訓練后，便可利用訓練好的隱含層權值與閾值對實際橋梁進行評估。Bp model can quite improve the accuracy of pricing result ; 2. need to confirm the number of the hidden layer of neuron rationally ; 3. should confirm population size rationally while optimizing ann ; 4
基本結論如下： （ 1 ） bp模型能夠提高定價結果的準確度；武漢理工大學碩士學位論文（ 2 ）建模需要合理確定隱含層神經元數目; （ 3 ）優化網路應恰當確定初始群體規模; （ 4 ）優化網路需要合理設計交叉運算元。The aim to study the system structure is to have better understanding, description and control of the complex system. the current studies focus on the system structure in a general way rather than differentiate the inner structure from the analytic structure of a system
結合復雜系統特徵和認知的階段與層次，本文在分析ahp和anp內在作用機制的基礎上分別給出了系統分析結構表達的方法即具有隱含層的分析結構。In the network, the input node is 64, the middle is 20 and the output is 4. we also use matlab train and simulate the designed network. finally, we designed software, which combines all the correlate theory and method list above to validate the thinking
神經網路在漢字識別中的應用包括研bp神經網路及其改進演算法、設計漢字識別所需要的bp神經網路，即在神經網路的輸入層、中間層、隱含層採用64 ? 20 ? 4的結構，並利用matlab6 . 5對所設計方案進行模擬和驗證。Multi - rules neural network learning part decreases the dimensions of attribute collection, to reach the goal of simplifying the input ; we stress the multi - rules learning algorithm based on fuzzy entropy rule ; at the same time, all the knowledge available is used to design the input layer, hidden layer and output layer of the neural network
多準則神經網路部分對客戶屬性集進行維數約簡，重點介紹了以模糊熵準則為基礎的多準則學習方法，同時提出了網路輸入層、隱含層及輸出層的構造方法。In training of back - propagation neural network, parameter adaptable method which can automatically adjust learning rate and inertia factor is employed in order to avoiding systemic error immersed in a local minimum and accelerating the network ' s convergence ; introduced the further optimization of the network ' s structure, it gives the research result of selection of the hidden layers, neurons, and the strategy of re - learning, compared the sums of the deviation square of this algorithm with conventional bp algorithm, as a result, the approach accuracy and the generalization ability of the network were extremely improved
在對前饋神經網路的訓練中，使用參數自適應方法實現了學習率、慣性因子的自我調節，以避免系統誤差陷入局部最小，加快網路的收斂速度；提出了優化bp網路結構的實驗研究方法，並給出了有關隱含層數和節點數選擇以及再學習策略引進的研究結果。將該演算法同傳統bp演算法的預測偏差平方和進行比較，結果證實網路的逼近精度及泛化能力均得到了極大的提高和改善。In the second is about the ann ' s structure, one genetic arithmetic ( ga ) is used to chose the most logical number of connotative layer, so it can void the blindness of chose the number of connotative layer. because based on more logical net structure, the precision of forecasting is improved
第二部分研究神經網路的結構問題，利用遺傳演算法對網路的隱含層個數進行尋優，從而避免了隱含層選擇的盲目性，使得預測在更加合理的網路結構上進行，提高了預測的精度。Higher - order neural network of single _ layer has no trouble of hiding layer node, so training speed is fast ; ability of pattern classified increases ; the classification accuracy of pattern is improved without local optimality problem ; hierarchy can make training speed of each node neural network, and improve the classified accuracy of pattern
單層高階神經網路沒有隱含層節點的困擾，訓練速度更快；模式分類能力更為強大；不存在局部最小的問題，模式分類精度更高；模式的不變性構建於網路結構之中等優點。分層方法能加快各節點神經網路的學習速度，也能提高模式劃分精度。分享友人