IEEE 802.11i-2004 Amendment to IEEE Std 802.11, 1999 Edition (Reaff 2003). IEEE Standard for Information technology--Telecommunications and information exchange between system--Local and metropolitan area networks?Specific requirements--Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications--Amendment 6: Medium Access Control (MAC) Security Enhancements
標簽： IEEE 802.11 Amendment Standard
上傳時間： 2013-12-21
上傳用戶：ywqaxiwang
IEEE 802.11j-2004 IEEE Standard for Information technology—Telecommunications and information exchange between systems--Local and metropolitan area networks—Specific requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications—Amendment 7: 4.9 GHz–5 GHz Operation in Japan
標簽： Telecommunications IEEE Information information
上傳時間： 2014-01-17
上傳用戶：asasasas
Aspect-Oriented Software Developement Coverage includes Using AOSD to streamline complex systems development without sacrificing flexibility or scalability How AOSD builds on the object-oriented paradigmand how it s different State-of-the-art best practices for the AOSD development process Languages and foundations: separating concerns, filter technologies, improving modularity, integrating new features, and more Using key AOSD tools, including AspectJ, Hyper/J, JMangler, and Java Aspect Components Engineering aspect-oriented systems: UML, concern modeling and elaboration, dependency management, and aspect composition Developing more secure applications with AOSD techniques Applying aspect-oriented programming to database systems Building dynamic aspect-oriented infrastructure
標簽： Aspect-Oriented Developement streamline Software
上傳時間： 2013-12-01
上傳用戶：jennyzai
Apriori源代碼，包含c++/java實現；<br>神經網絡算法源程序，如SOM、HOPFIELD、CPN、BPN、BOLTZMAN、ART、ADALINE，同時提供針對不同算法的演示源程序；遺傳C源代碼；外加充實的數據挖掘的算法講解ppt
上傳時間： 2014-01-23
上傳用戶：zxc23456789
RECOMMENDATION ITU-R M.1653*,** Operational and deployment requirements for wireless access systems including radio local area networks in the mobile service to facilitate sharing between these systems and systems in the Earth exploration-satellite service (active) and the space research service (active) in the band 5 470-5 570 MHz within the 5 460 5 725 MHz range
標簽： RECOMMENDATION requirements Operational deployment
上傳時間： 2016-10-21
上傳用戶：miaochun888
另一本介紹貝葉斯網絡的經典教材，可以與Learning Bayesian Networks配合使用，相得益彰。
上傳時間： 2014-01-05
上傳用戶：電子世界
* Lightweight backpropagation neural network. * This a lightweight library implementating a neural network for use * in C and C++ programs. It is intended for use in applications that * just happen to need a simply neural network and do not want to use * needlessly complex neural network libraries. It features multilayer * feedforward perceptron neural networks, sigmoidal activation function * with bias, backpropagation training with settable learning rate and * momentum, and backpropagation training in batches.
標簽： backpropagation implementating Lightweight lightweight
上傳時間： 2013-12-27
上傳用戶：清風冷雨
％ 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 applies the Optimal Brain Surgeon (OBS) strategy for ％ pruning neural network models of dynamic systems. That is networks ％ trained by NNARX, NNOE, NNARMAX1, NNARMAX2, or their recursive ％ counterparts.
標簽： function strategy Optimal Surgeon
上傳時間： 2013-12-19
上傳用戶：ma1301115706
Train a two layer neural network with a recursive prediction error ％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully ％ connected) networks can be trained. ％ ％ The activation functions can either be linear or tanh. The network ％ architecture is defined by the matrix NetDef , which has of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer.
標簽： recursive prediction algorithm Gauss-Ne
上傳時間： 2016-12-27
上傳用戶：ljt101007
