hidden node 中文意思是什麼
hidden node
解釋
隱藏節點-
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。 -
We deduce frondose algorithm of three layers bp neural networks which is used in common, and discuss several important issues in designing neural networks which is used to forecast, for example, number of hidden layer, nerve cell number of hidden layer, epoch of learning, embryonic power value, decision of node number about input and outputo at the same time, this dissertation sums up things that conventional bp algorithm is improved on considering disadvantages of it
3推導了常用的三層bp神經網路具體演算法，討論了實際預測應用中神經網路設計方面的幾個重要問題，如隱層數、隱層神經元數、訓練次數、初始權值、輸入節點數以及輸出節點數的確定。同時，針對傳統bp演算法存在的各種各樣的缺點，文中綜述了對其改進的情況。 -
Keeping the original structure of cbp unchanged, we add an extra node to cbp input level and assign specific values to the weights between that node and the hidden level, and then we obtain a more general model than cbp, which is icbp
我們在保持cbp原有結構下，通過對cbp的輸入層擴充節點（ 1個）的特殊構造，以及對該節點與隱層間權值的特殊賦值，獲得了較cbp更一般的改進網路模型（ icbp ） 。 -
In course of training the network, the structure of the network has been analysed. using the nonlinear theory, the node of the hidden layer is ascertained. by comparing this method is very efficient
（ 5 ）在網路訓練過程中，對神經網路結構進行了分析，建立了計算輸出和理想輸出關系非線性方程組，依據非線性方程理論闡述設計變量、樣本數量和輸出層單元數量的關系，確定了隱層神經元的數量，經比較該方法很有效。 -
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。 -
This article proposes in order to quickly hidden processing is carried on to multi concavo - convex polyhedron, the hierarchical structure is applied to carried on description on topo relations of three dimensional physique ; the bidirectional link table is applied to carried on the dynamic node assignment, for enhancing the efficiency of the algorithm ; when multi three - dimensional body is being hidden processing, the screen projection polygon should be carried on the overlaped eliminating confirmation and the polygon edges be carried on the computation of the intersectant points, the depth inspection, the comprehensive test and so on
摘要文章提出一種對多個凹凸形多面體進行消隱處理演算法，並介紹了採用層次結構進行三維形體的拓撲關系描述；採用雙向鏈表進行動態結點分配的演算法，提高了演算法效率；對多個三維物體消隱處理時，進行屏幕投影多邊形的重疊排除驗證，對多邊形邊進行交點計算，深度檢查，包容性測試等。 -
( 2 ) combining secondary genetic algorithm with back - propagation network, the thesis redacts genetic neural network procedure, which optimizes number of hidden node and weight value and threshold value simultaneously. the procedure overcomes blindness during search, avoids falling into localminimum and increases learning accuracy
（ 2 ）編寫了遺傳神經網路ga - bp程序，採用二級遺傳演算法與bp演算法相結合，同時優化網路隱層節點數和權值、閾值，既克服了尋優過程的盲目性，又避免陷入局部極小，提高了網路的學習精度。 -
After designing the hidden - layer structure of network, the neural network data - mending program is developed with matlab, and with the contrast of results gained from single and double hidden - layer the number of hidden - layer is selected. after the training algorithm is studied, using the visual application - designing tool, visual basic, the application adopting the designed algorithm is developed. with plenty of experimental study, the node number of the hidden - layer is decided and the rational algorithm is designed, which can advance the speed of training network, and decrease the probability of running into the local teeny value
在研究網路隱層結構后，用matlab編制了神經網路修補程序，對比單雙隱層結構的修補結果，確定了修補用bp神經網路的隱層數；在研究網路訓練演算法后，利用可視化應用程序開發工具visualbasic ，編制了相應的應用程序，通過大量的實驗研究，確定了網路隱層節點數，設計了合理的網路訓練演算法，提高了網路的訓練速度，減小了網路訓練陷入局部極小的可能性。 -
Where d for a hidden node n, turns on how much n influences any given output node ; and how much that output node itself influences the overall error of the net
其中d （ n ）是隱藏節點n的函數，讓我們來看（ 1 ） n對任何給出的輸出節點有多大影響； （ 2 ）輸出節點本身對網路整體的誤差有多少影響。 -
Limiting ourselves to nets with no hidden nodes, but possibly having more than one output node, let p be an element in a training set, and t be the corresponding target of output node n
將我們的網路限制為沒有隱藏節點，但是可能會有不止一個的輸出節點，設p是一組培訓中的一個元素， t （ p ， n ）是相應的輸出節點n的目標。 -
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