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<li id="ksqcq"></li> 學上的基本神經元，人工的神經網絡也有基本的神經元。每個神經元有特定數量的輸入，也會為每個神經元設定權重（Weight）。權重是對所輸入的資料的重要性的一個指標。然后，神經元會計算出權重合計值（net value），而權重合計值就是將所有輸入乘以它們的權重的合計。每個神經元都有它們各自的臨界值（threshold），而當權重合計值大于臨
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上傳時間： 2014-06-06
上傳用戶：luke5347
j2me設計的界面包，很漂亮實用。 light Weight UI toolkit
標簽： j2me
上傳時間： 2013-12-21
上傳用戶：kristycreasy
編寫一個Java程序，設計一個運輸工具類Transport，包含的成員屬性有：速度pace、載重量load；汽車類Vehicle是Transport的子類，其中包含的屬性有：車輪的個數wheels和車重Weight；飛機Airplane類是Transport的子類其中包含的屬性有：機型enginertype和發動機數量enginers。每個類都有相關所有數據的輸出方法。
上傳時間： 2016-11-16
上傳用戶：miaochun888
This function calculates Akaike s final prediction error ％ estimate of the average generalization error. ％ ％ [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the ％ final prediction error estimate (fpe), the effective number of ％ Weights in the network if the network has been trained with ％ Weight decay, an estimate of the noise variance, and the Gauss-Newton ％ Hessian. ％
標簽： generalization calculates prediction function
上傳時間： 2014-12-03
上傳用戶：maizezhen
％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is possible to use regularization by ％ Weight decay. Also pruned (ie. not fully connected) networks can ％ be trained. ％ ％ Given a set of corresponding input-output pairs and an initial ％ network, ％ [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms) ％ trains the network with the Levenberg-Marquardt method. ％ ％ The activation functions can be either linear or tanh. The ％ network architecture is defined by the matrix NetDef which ％ has two rows. The first row specifies the hidden layer and the ％ second row specifies the output layer.
標簽： Levenberg-Marquardt desired network neural
上傳時間： 2016-12-27
上傳用戶：jcljkh
This function calculates Akaike s final prediction error ％ estimate of the average generalization error for network ％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive ％ counterparts. ％ ％ [FPE,deff,varest,H] = nnfpe(method,NetDef,W1,W2,U,Y,NN,trparms,skip,Chat) ％ produces the final prediction error estimate (fpe), the effective number ％ of Weights in the network if it has been trained with Weight decay, ％ an estimate of the noise variance, and the Gauss-Newton Hessian. ％
標簽： generalization calculates prediction function
上傳時間： 2016-12-27
上傳用戶：腳趾頭
learning English The following appeared in a memorandum written by the vice president of Nature s Way, a chain of stores selling health food and other health-related products. "Previous experience has shown that our stores are most profitable in areas where residents are highly concerned with leading healthy lives. We should therefore build our next new store in Plainsville, which has many such residents. Plainsville merchants report that sales of running shoes and exercise clothing are at all-time highs. The local health club, which nearly closed five years ago due to lack of business, has more members than ever, and the Weight training and aerobics classes are always full. We can even anticipate a new generation of customers: Plainsville s schoolchildren are required to participate in a fitness for life program, which emphasizes the benefits of regular exercise at an early age.
標簽： memorandum following president learning
上傳時間： 2017-03-06
上傳用戶：youth25
In this project we analyze and design the minimum mean-square error (MMSE) multiuser receiver for uniformly quantized synchronous code division multiple access (CDMA) signals in additive white Gaussian noise (AWGN) channels.This project is mainly based on the representation of uniform quantizer by gain plus additive noise model. Based on this model, we derive the Weight vector and the output signal-to-interference ratio (SIR) of the MMSE receiver. The effects of quantization on the MMSE receiver performance is characterized in a single parameter named 鈥漞quivalent noise variance鈥? The optimal quantizer stepsize which maximizes the MMSE receiver output SNR is also determined.
標簽： mean-square multiuser receiver project
上傳時間： 2014-11-21
上傳用戶：ywqaxiwang
LwIP是瑞士計算機科學院(Swedish Institute of Computer Science)的AdamDunkels等開發的一套用于嵌入式系統的開放源代碼TCP/IP協議棧。LwIP的含義是Light Weight(輕型)IP協議。LwIP可以移植到操作系統上，也可以在無操作系統的情況下獨立運行。LwIP協議的基礎是在減少對硬件資源占用的前提下完成TCP/IP協議的主要功能
標簽： LwIP AdamDunkels Institute Computer
上傳時間： 2014-11-04
上傳用戶：love1314
NN Functions a program in Lisp to demonstrate working of an artificial neuron. (Enter an input vector X and Weight vector W. Calculate Weighted sum XW. Transform this using signal or activation functions like logistic, threshold, hyperbolic-tangent, linear, exponential, sigmoid or some other functions (syntax provided) and display the output).
標簽： demonstrate artificial Functions program
上傳時間： 2013-12-30
上傳用戶：hfmm633
