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.
％
％ 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.
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.
％
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.
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.
LwIP是瑞士計(jì)算機(jī)科學(xué)院(Swedish Institute of Computer Science)的AdamDunkels等開發(fā)的一套用于嵌入式系統(tǒng)的開放源代碼TCP/IP協(xié)議棧。LwIP的含義是Light weight(輕型)IP協(xié)議。LwIP可以移植到操作系統(tǒng)上，也可以在無操作系統(tǒng)的情況下獨(dú)立運(yùn)行。LwIP協(xié)議的基礎(chǔ)是在減少對(duì)硬件資源占用的前提下完成TCP/IP協(xié)議的主要功能
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).
