P - perceptron 中文

perceptron n.【無線電】視感控器〔類似視神經的電子儀器〕。

perceptual
At first the creation of typical perceptron model and its learn algorithm are introduced . then on the base of the principal and geometrical presentation of typical perception model , the limitations of typical perceptron model are studied . this limitation is that perceptron learn algorithm can be used only when data are linear separability 在神經網絡中，重點分析研究了感知器基本模型，包括感知器基本模型的構造及其學習算法，模型的幾何意義及其局限性，并針對該模型只有在線性可分的情況下才能用感知器的學習算法進行分類的這一固有局限性，研究并推廣了感知器模型。
If we concatenate the recognition results of each sensors and consider it as a joint feature vector of target , then decision level fusion for target recognition can be considered as a conventional pattern recognition problem . this dissertation uses multilayer perceptron to classify the joint feature vector 將各傳感器輸出的識別結果連接起來作為目標的一個聯合特征矢量，則決策層融合目標識別可看作是一個常規的模式識別問題，本文采用多層感知器網絡對這個聯合特征矢量進行分類，實現決策層融合目標識別。
Two distinct bp training algorithms on multilayer perceptron type neural - network are developed to improve the trained network ' s quickness , robustness and generality . they are brought out from the combination of existing investigations - dynamic learning rate , momentum terms and quadratic function of weights , and amendment of desired error function respectively . the simulative results of rotating machinery fault pattern classification illustrated the effectiveness of the new algorithms 針對多層感知器神經網絡的標準bp訓練算法存在推廣能力差和訓練速度慢等缺陷，首先結合已有的研究成果，給出了一種動態學習率和動量項相結合，并在誤差函數中引入網絡權值的二次型函數項的改進訓練算法，提高了網絡的快速性和推廣能力。
We use three types of algorithm : perceptron network , back - propagation ( b - p ) network and self - organizing feature maps ( som ) network . using the data in form of 64 - channel spectra as inputs , the ann presents the analysis and estimation results of the oil type on the basis of the type of background materials as outputs 而在眾多的人工神經網絡模型中，本論文選擇了三種模式識別應用最普遍效果相對較好的網絡模型，即感知器、 bp 、 som網絡，提出了適用于海面溢油激光遙感光譜的智能分析與識別的神經網絡理論模型。
This paper begins with the restrictions of the - existent net - flow modelling methods . integrated with the characteristics of modern net - flow system and utilizing francesco luna ' s idea of pcrceptron and xor function , we express human ' s consciousness with logistic function of perceptron , and the uncertain relations among nodes are mapped to xor function . on the basis of these , a new modelling method of net - flow is offered 本文從已有網流系統建模方法的局限性出發，結合現代網流系統的特征，利用francescoluna的感知器與異或問題的思想，將人的意識用感知器對應的邏輯函數進行描述，節點之間連接關系的不可感知性映象為異或函數，提出網流系統的一種新的建模方法。
For the planar gap and the tridimensional vertical bend of shielded cpw , fdtd simulations are carried out to produce training and testing samples and error - back propagation algorithm is used to train the multilayer perceptron neural networks ( mlpnns ) . rapid and accuracy cad models of these structures are successfully obtained for the first time 對于屏蔽cpw的間隙不連續性和垂直互連不連續性結構，采用fdtd方法獲取訓練和檢測樣本數據，用回傳算法訓練多層感知器，首次成功地獲得了這些結構快速、準確的cad模型。
Perceptron , relaxation , mse and ho - kashyap ( hk ) algorithm . hk is not robust to outliers . the modified hk with square approximation of the misclassification errors ( mhks ) tries to avoid this shortcoming and adopts similar principle to the support vector machine to maximize the separation margin 線性分類器因其簡單、易于分析和實現且容易推廣為非線性分類器的優點而成為模式分類最常用的分類器,并產生了感知器( perceptron ) 、松弛算法( relaxation ) 、最小平方誤差( minimumsquareerror , mse )和ho - kashyap ( h - k )算法等經典算法。
In this dissertation , base on the review and analysis of current mainstream algorithms and techniques , we build up whole rtr system , and study some efficient recognition methods for different radar characters . the major research work and contributions in this dissertation are summarized as below : 1 . summarized current popular pattern recognition methods , this dissertation researched in the algorithms and performances of nearest neighbor ( nn ) classifier , multi - layer perceptron and rbfn ( radial basis function network ) 本文在總結當前主流雷達目標識別算法的基礎上,建立起基于gbr的雷達目標識別系統,并且對彈道導彈的各種特識別方法進行了研究,主要進行的工作和創新有: 1 .研究和總結了當前常用的分類識別方法,針對雷達目標識別的特點,對近鄰分類器、多層感知器和徑向基函數網絡( rbfn )分類器的算法和性能進行了研究。
This paper aims to combine advantages of pid control and neuron , propose the neuron pid controller which is derived from an incomplete derivative pid algorithm and based on six learning rules in common use , viz . no surpervized hebbian learning rule , perceptron learning rule , supervized learning rule , improved hebbian learing rule , delta learning rule and capability index which is based on second type , and these rules come into being six control arithmatic . then simulate in object with lag 本論文主要將兩者的優點結合,提出了神經元實現不完全微分pid ,并采用神經網絡常用的六種學習規則,即無監督hebb學習規則、感知器的學習規則、有監督的hebb學習規則、改進的hebb學習規則、 delta學習規則和基于二次型性能指標的學習規則,形成六種控制算法,以工業生產過程中常見的二階純滯后對象為例進行仿真。
Two spatially registered images with different focuses are decomposed into several blocks . then , three features reflecting the clear level of every block , i . e . , spatial frequency , visibility , and edge , are calculated . finally , artificial neural networks , i . e . , multilayer - perceptron , radial - basis function , probabilistic neural network , are used to recognize the clear level of the corresponding blocks to decide which blocks should be used to construct the fusion result 具體實現過程概述如下：首先將兩幅（或多幅）配準圖象進行分塊處理，提取兩幅圖象中對應塊的能反映圖象清晰度的三種特征，即空間頻率、可見度和邊緣，將特征歸一化后送入訓練好的神經網絡進行識別，根據得到的結果依據“誰清晰誰保留”的原則構成融合的圖象。
For short - time natural gas load forecasting . based on analyzing tech situation at home and abroad , considering all kinds of factors which will have influence on load changes , a hybrid approach combined the self - organizing feature map ( sofm ) neural network with multilayer perceptron ( mlp ) is presented , and short - time load forecasting model is established 針對大然氣短期負荷預測的問題，在分析了國內外技術現狀的基礎上，綜合考慮影響負荷變化的各種因素，提出了基于白織織競爭網絡和多層感知機網絡棍合的大然氣短期負荷預測方法。
The microsoft neural network algorithm creates classification and regression mining models by constructing a multilayer perceptron network of neurons , providing support for nonlinear models that are too complex to derive by using other algorithms Microsoft神經網絡算法通過構造神經元的多層感知器網絡來創建分類和回歸挖掘模型，同時為過于復雜而無法使用其他算法派生的非線性模型提供支持。
Is that if a set of points in n - space is cut by a hyperplane , then the application of the perceptron training algorithm will eventually result in a weight distribution that defines a tlu whose hyperplane makes the wanted cut ）下的結論是，如果n維空間的點集被超平面切割，那么感知器的培訓算法的應用將會最終導致權系數的分配，從而定義了一個tlu ，它的超平面會進行需要的分割。
The perceptron 3d scanning solutions group is dedicated to providing high value software and sensor based 3d scanning solutions for new perceptron clients in industries such as automotive , aerospace , and steel 感知三維掃描解組,是專門用來提供高價值的軟件和傳感器的三維掃描解新的感知客戶的行業,如汽車,航空,鋼鐵等
Based on analyzing the multilayer perceptron neural network , it induces the error backpropagation algorithm while taking the hyperboloid tangential function as nonlinear activation function 在分析多層感知器神經網絡的基礎上，推導了雙曲正切函數為激勵函數的誤差后向傳播算法。
Based on the single - layer perceptron model , a relation between sample size and error classifying for the design of fault classifier for the afr engine is given 摘要針對基于單層感知器模型的發動機故障進行分類器設計，研究了故障信號的學習樣本容量和分類誤判率之間的關系。
Based on rbf neural network and perceptron neural network , a four - layer feed - forward neural network named radial basis perceptron ( rbp ) network is presented 基于rbf網絡和感知器( perceptron )網絡建立一四層前饋神經網絡?徑向基感知器( radialbasisperceptron ， rbp )網絡。
The perceptron training rule is based on the idea that weight modification is best determined by some fraction of the difference between target and output 感知器培訓規則是基于這樣一種思路權系數的調整是由目標和輸出的差分方程表達式決定。
The microsoft neural network algorithm creates classification and regression mining models by constructing a multilayer perceptron network of neurons Microsoft神經網絡算法通過構建多層感知器網絡來創建分類和回歸挖掘模型。

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perceptron

perceptron n.【無線電】視感控器〔類似視神經的電子儀器〕。

perceptual

The perceptron training rule is based on the idea that weight modification is best determined by some fraction of the difference between target and output 感知器培訓規則是基于這樣一種思路權系數的調整是由目標和輸出的差分方程表達式決定。

The microsoft neural network algorithm creates classification and regression mining models by constructing a multilayer perceptron network of neurons Microsoft神經網絡算法通過構建多層感知器網絡來創建分類和回歸挖掘模型。