hidden layers 中文意思是什麼
hidden layers
解釋
隱藏的圖層-
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。 -
It is very important to estimate the basic parameters in helicopter preliminary design. neural network ( nn ) has the advantages in estimating accuracy and generalization over traditional methods. however, there are some difficulties in using nn, e. g., how to select a proper network structure and the number of hidden layers. in this paper, structure and connection weight of a three - layer nn are optimized by genetic algorithm, and the optimized network is applied to helicopter sizing. the proposed method can not only give an optimal nn structure and connection weight, but also reduce the prediction error and has the capability of self - learning when the latest data are available. furthermore, this method can be easily applied to helicopter design systems
在直升機初步設計階段估算其基本參數是很重要的.神經網路的通用性和精度比傳統的估算方法有更多的優勢，但是在應用神經網路時存在如何選擇合適的網路結構和隱層節點數目等一些困難.應用遺傳演算法優化三層神經網路結構和連接權重，並將優化得到的網路應用於直升機參數選擇中.該方法不但可以給出一個最優的神經網路結構和連接權重，而且降低了估算誤差，具有及時應用最新數據學習的能力.此外，該方法易於在直升機設計系統中得到應用 -
The science of language has large and close analogies in geological science, with its ceaseless evolution, its fossils, and its numberless submerged layers and hidden strata, the infinite go - before of the present
語言科學非常近似地質科學，因為它也處于永不停息的發展演變之中，也有自己的化石、有自己無數淹沒的巖層和深?的地層，以及無窮無盡的過去。 -
In the second layer, k - nearest neighbor algorithm is introduced to ascertain searching scope firstly, and then the nerve cell function ' s parameter in hidden layers begin to be evolved in this scope. the least - square is also introduced to calculate connection power between hidden layer and output layer
其中在第二級演化中，先用最小鄰聚法確定搜索空間，然後再在此空廣西大學頎十論文i 13f神經網路在ect圖像重注中的應用研穴間內進行演化，其中用最小二乘法來確定從隱層到輸出層的連接權值。 -
Through the all - sided tectonic analyses, it can be deduced that there are two aspects will be the hidden defects to the dam and the engineering stability of the reservoir area. one is the dislocation interfaces resulting from the the majiaheba faultage ' s thrusting overriding and other causes of formation. the second is the region of strong deformation, such as the above of the dam, middle and high positions of the lava layers and the regions of overprint of the structure of ne to the one of nw, which should be taken into account especially
通過區域和壩區錯動帶的全面構造解析，認為由馬家河壩斷層逆沖推覆作用而在壩區形成的向金沙江下游緩傾的錯動帶以及其它成因的錯動帶是壩區和庫區工程穩定的隱患，尤其是變形較強的壩址區上游區、中高層位以及北西向構造期的錯動帶疊加於北東向構造期錯動帶的部位，更應該引起足夠的重視。 -
Function : it can eliminate dirt, aged cuticle layers, and dead cells deeply hidden in the skin pore, natural nutritional ingredient can moisturize the skin, make your skin bright and display beauty of youth.
功能：將深藏在毛孔內的污垢老化角質枯死細胞徹底消除，天然營養成分能深入滋潤肌膚，令肌膚青春永駐亮麗照人。 -
The full - rank matrix is employed to find the complex - valued weights between hidden and output layers by the least mean square algorithm
利用這個滿秩矩陣，通過最小平方演算法就可以求得隱層和輸出層之間的復數權值。 -
The number of the hidden layers of mul - tilayer perceptrons ( mlps ) is analyzed, and three - layer perceptrons neural network is adopted ; by analyzing the mechanism of the neural cells in hidden layer, a method for combining genetic algorithm and bp algorithm to optimize the design of the neural networks is presented, and it solves the defects of getting into infinitesimal locally and low convergence efficiently, it can also solve the problem that it can usually obtain nearly global optimization solution within shorter time through using genetic algorithm method lonely ; several examples validate that this algorithm can simplify the neural networks effectively, and it makes the neural networks solve the practical problem of fault diagnosis more effectively
對多層感知器隱層數進行了分析，確定採用三層感知器神經網路;通過對隱層神經元作用機理的分析，引入了遺傳演算法與bp演算法相結合以優化設計神經網路的方法，有效地解決了bp演算法收斂速度慢和易陷入局部極小的弱點，還可以解決單獨利用遺傳演算法往往只能在短時間內尋找到接近全局最優的近優解的問題;通過實例驗證了這種演算法能夠有效地簡化神經網路，使神經網路更加有效地解決實際的故障診斷問題。 -
Open animation window. for the first frame let the both text layers be hidden behind their masks. reduce the opacity of radial waves to 50 %
打開動畫窗口。第一幀將兩個文字的圖層都隱藏在蒙版後面，將手機信號線的透明度減少到50 % 。 -
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。 -
The dalinghe hidden fault, as an example in this paper, is just located under the dalinghe river, so the activity of this hidden fault can be defined by comparing quaternary layers and terraces beside the river hidden fault. based on spore analysis of 17 layers and several
以大凌河隱伏斷裂為實例，根據斷裂位於大凌河床底部的特點，通過對比河流斷裂兩側的第四紀地層和階地，對隱伏斷裂的活動性進行研究。孢粉分析和 -
When the number of hidden layers was 2, the classified accuracy was the best in any kind of mixed samples
但當以不同隱藏層處理單元數進行模擬試驗所得結果，以隱藏層個數為2時，分類正確率都是最佳的。 -
The purpose of this study was to explore the effect of the different training samples, learning rate and numbers of hidden layers on the classified accuracy when using learning vector quantization to analysis
摘要本研究采電腦模擬探討不同訓練範例樣本數、學習速率及隱藏層個數對學習向量量化網路分類正確率之影響。 -
Abstract : it is very important to estimate the basic parameters in helicopter preliminary design. neural network ( nn ) has the advantages in estimating accuracy and generalization over traditional methods. however, there are some difficulties in using nn, e. g., how to select a proper network structure and the number of hidden layers. in this paper, structure and connection weight of a three - layer nn are optimized by genetic algorithm, and the optimized network is applied to helicopter sizing. the proposed method can not only give an optimal nn structure and connection weight, but also reduce the prediction error and has the capability of self - learning when the latest data are available. furthermore, this method can be easily applied to helicopter design systems
文摘:在直升機初步設計階段估算其基本參數是很重要的.神經網路的通用性和精度比傳統的估算方法有更多的優勢，但是在應用神經網路時存在如何選擇合適的網路結構和隱層節點數目等一些困難.應用遺傳演算法優化三層神經網路結構和連接權重，並將優化得到的網路應用於直升機參數選擇中.該方法不但可以給出一個最優的神經網路結構和連接權重，而且降低了估算誤差，具有及時應用最新數據學習的能力.此外，該方法易於在直升機設計系統中得到應用 -
The main conclusions are as follows : through the different structure and algorithm application of bp model in the predication of regional groundwater hydrology, the hidden layers number, learning rates, neuron number of hidden layer and training errors of bp model and accelerated bp algorithm which influence the convergence effects and test results of model are compared each other. some application technology related parameters of bp structure design are put forward
論文取得了以下主要成果：通過不同bp網路結構和演算法在區域地下水文預測中的實例研究，重點比較了不同層次結構、隱層單元數、學習速率、訓練收斂誤差等4個基本要素及不同演算法、不同樣本容量等對模型收斂效果、模擬、檢驗與預報結果的具體影響。 -
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。 -
Nonlinear dynamic modelling of sensors is an important aspect in the field of instrument technique. the recursive neural network is proposed for nonlinear dynamic modelling of sensors, as its architecture is determined only by the number of nodes in the input, hidden and output layers. with the feedback behavior, the recursive neural network can catch up with the dynamic response of the system. the recursive neural network which involves dynamic elements and feedback connections has important capabilities that are not found in feedforward networks, such as the ability to store information for later use and higher predicting precision. a recursive prediction error algorithm which converges fast is applied to training the recursive neural network. experimental results show that the performance of the recursive neural network model conforms to the sensor to be modeled, and the method is not only effective but of high precision
根據動態校準實驗結果建立傳感器的動態數學模型，以研究傳感器的動態性能，是動態測試的一個重要內容.討論了遞歸神經網路模型在傳感器動態建模中的應用，給出了遞歸神經網路模型的結構及相應的訓練演算法.由於其反饋特徵，使得遞歸神經網路模型能獲取系統的動態響應.該方法特別適用於傳感器非線性動態建模，而且避免了傳感器模型階次的選擇的困難.試驗結果表明，應用遞歸神經網路對傳感器進行動態建模是一種行之有效的方法 -
In actual application the data of different area have different characteristic, when the hidden layers and input nerve centers are less, we use f - psa combining with bp to predict oil reservoir parameters ; otherwise we use f - gsa combining with bp to predict oil reservoir parameters. in this thesis some emulation results and actual application results are offered
Gsa ，並將它們與bp網路相結合應用到薄互油藏參數的預測中。對于不同地區，由於地區性差異，當根據實際情況，網路隱層和輸入神經元較少時，將fpsa與bp網結合，完成參數預測。 -
This network can be summarized as follows : ( 1 ) about the networks " architecture, it is not fully connected but it uses selective connection between the units of two hidden layers. the number of these units is determined dynamically. ( 2 ) during the learning procedure, a new input - output clustering ( ioc ) method is adopted to select centers
該網路主要有以下特點： （ 1 ）網路結構上，兩層隱層選擇性連接，隱層節點數在學習過程中動態確定； （ 2 ）學習規則上，提出一種同時考慮輸入輸出樣本信息的輸入一輸出聚類（ input - outputclustering ， ioc ）方法，且聚類中心的形狀參數自適應變化。
分享友人