j2me设计的界面包，很漂亮实用。 light weight UI toolkit
标签： j2me
上传时间： 2013-12-21
上传用户：kristycreasy
* The keyboard is assumed to be a matrix having 4 rows by 6 columns. However, this code works for any * matrix arrangements up to an 8 x 8 matrix. By using from one to three of the column inputs, the driver * can support "SHIFT" keys. These keys are: SHIFT1, SHIFT2 and SHIFT3.
标签： keyboard However assumed columns
上传时间： 2016-11-14
上传用户：ardager
编写一个Java程序，设计一个运输工具类Transport，包含的成员属性有：速度pace、载重量load；汽车类Vehicle是Transport的子类，其中包含的属性有：车轮的个数wheels和车重weight；飞机Airplane类是Transport的子类其中包含的属性有：机型enginertype和发动机数量enginers。每个类都有相关所有数据的输出方法。
上传时间： 2016-11-16
上传用户：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. ％
标签： generalization calculates prediction function
上传时间： 2014-12-03
上传用户：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.
标签： Levenberg-Marquardt desired network neural
上传时间： 2016-12-27
上传用户：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. ％
标签： generalization calculates prediction function
上传时间： 2016-12-27
上传用户：脚趾头
This software is a Matlab implementation of restricted sampling from Gaussian distribution, and sample x (column vector) from N(x_mu, x_var), restricted in x_min<=x<=x_max.
标签： implementation distribution restricted Gaussian
上传时间： 2016-12-30
上传用户：6546544
本文档介绍了如何使用各种内嵌工具，函数和其他一些小技巧来加强使用matlab的速度和效率，是广大爱好者必读的文档。具体内容请参阅文档。 Learn how to use the Profiler tool, vectorized functions, and other tricks to writing efficient MATLAB code. This article includes how to convert any array into a column vector, bounding a value without if statements, and repeating/tiling a vector without repmat. Contents: * The Profiler * Array Preallocation * JIT Acceleration * Vectorization * Inlining Simple Functions * Referencing Operations * Numerical Integration * Signal Processing * Miscellaneous Tricks
上传时间： 2013-12-11
上传用户：cuiyashuo
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.
标签： memorandum following president learning
上传时间： 2017-03-06
上传用户：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.
标签： mean-square multiuser receiver project
上传时间： 2014-11-21
上传用户：ywqaxiwang
