the handbook of c and c ++, the most important tool for c & c++ user!
標(biāo)簽： the important handbook most
上傳時(shí)間： 2015-06-16
上傳用戶(hù)：anng
fastDNAml is an attempt to solve the same problem as DNAML, but to do so faster and using less memory, so that larger trees and/or more bootstrap replicates become tractable. Much of fastDNAml is merely a recoding of the PHYLIP 3.3 DNAML program from PASCAL to C.
標(biāo)簽： fastDNAml attempt problem faster
上傳時(shí)間： 2014-01-24
上傳用戶(hù)：bjgaofei
it is used to generate random number to simulate the conmmunication scheme, very useful
標(biāo)簽： conmmunication generate simulate random
上傳時(shí)間： 2013-11-27
上傳用戶(hù)：lunshaomo
This Program Is Designed To Simulate A Spatial Antenna Array System Working On The MUSIC Algorithm For The Angle Of Arrival Estimation And Null Steering Algorithm For The Weights Estimation To The Required Output Radiation Pattern .
標(biāo)簽： Designed Algorith Simulate Program
上傳時(shí)間： 2014-01-02
上傳用戶(hù)：三人用菜
This applet illustrates the prediction capabilities of the multi-layer perceptrons. It allows to define an input signal on which prediction will be performed. The user can choose the number of input units, hidden units and output units, as well as the delay between the input series and the predicted output series. Then it is possible to observe interesting prediction properties.
標(biāo)簽： capabilities illustrates multi-layer perceptrons
上傳時(shí)間： 2015-06-17
上傳用戶(hù)：lnnn30
The Hopfield model is a distributed model of an associative memory. Neurons are pixels and can take the values of -1 (off) or +1 (on). The network has stored a certain number of pixel patterns. During a retrieval phase, the network is started with some initial configuration and the network dynamics evolves towards the stored pattern which is closest to the initial configuration.
標(biāo)簽： model distributed associative Hopfield
上傳時(shí)間： 2015-06-17
上傳用戶(hù)：l254587896
Single-layer neural networks can be trained using various learning algorithms. The best-known algorithms are the Adaline, Perceptron and Backpropagation algorithms for supervised learning. The first two are specific to single-layer neural networks while the third can be generalized to multi-layer perceptrons.
標(biāo)簽： Single-layer algorithms best-known networks
上傳時(shí)間： 2015-06-17
上傳用戶(hù)：趙云興
The EM algorithm is short for Expectation-Maximization algorithm. It is based on an iterative optimization of the centers and widths of the kernels. The aim is to optimize the likelihood that the given data points are generated by a mixture of Gaussians. The numbers next to the Gaussians give the relative importance (amplitude) of each component.
標(biāo)簽： algorithm Expectation-Maximization iterative optimi
上傳時(shí)間： 2015-06-17
上傳用戶(hù)：獨(dú)孤求源
Six objects here: The document you re reading & its Chinese version Set of "HWDesign" includes all hardware design files "SWDesign" includes all software design files(firmware/driver and maybe applications as well) Set of "Documents" includes files of main part of thesis and its reference "Thesis" includes files used to academe.
標(biāo)簽： HWDesign document includes Chinese
上傳時(shí)間： 2015-06-17
上傳用戶(hù)：xiaodu1124
I know we can t forget the past you can t forget love and pride because of that,it s kill in my inside
標(biāo)簽： forget can because pride
上傳時(shí)間： 2015-06-19
上傳用戶(hù)：stvnash
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