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<li id="wyemm"></li> (Ebook - Mathematics) Development Of Neural Network Theory For Artificial Life-Thesis, Matlab And Java Code, Cavuto
標簽： Development Mathematics Life-Thesis Artificial
上傳時間： 2014-08-19
上傳用戶：xzt
A framework for supporting advance reservation service in GMPLS-based WDM networks.
標簽： GMPLS-based reservation supporting framework
上傳時間： 2017-05-11
上傳用戶：koulian
artificial intelligence Modelling brain human apply texture, color, effect neural network guide for standard techniques
標簽： intelligence artificial Modelling texture
上傳時間： 2017-07-28
上傳用戶：agent
ip based networks which provides basic knowledge on IP
標簽： knowledge networks provides based
上傳時間： 2017-07-29
上傳用戶：稀世之寶039
ADIAL Basis Function (RBF) networks were introduced into the neural network literature by Broomhead and Lowe [1], which are motivated by observation on the local response in biologic neurons. Due to their better approximation capabilities, simpler network structures and faster learning algorithms, RBF networks have been widely applied in many science and engineering fields. RBF network is three layers feedback network, where each hidden unit implements a radial activation function and each output unit implements a weighted sum of hidden units’ outputs.
標簽： introduced literature Broomhead Function
上傳時間： 2017-08-08
上傳用戶：lingzhichao
Abstract—Wireless networks in combination with image sensors open up a multitude of previously unthinkable sensing applications. Capable tools and testbeds for these wireless image sensor networks can greatly accelerate development of complex, yet efficient algorithms that meet application requirements. In this paper, we introduce WiSNAP, a Matlab-based application development platform intended for wireless image sensor networks. It allows researchers and developers of such networks to investigate, design, and evaluate algorithms and applications using real target hardware. WiSNAP offers standardized and easy-to-use Application Program Interfaces (APIs) to control image sensors and wireless motes, which do not require detailed knowledge of the target hardware. Nonetheless, its open system architecture enables support of virtually any kind of sensor or wireless mote. Application examples are presented to illustrate the usage of WiSNAP as a powerful development tool.
標簽： combination previously multitude Abstract
上傳時間： 2013-12-03
上傳用戶：D&L37
*** *** *** *** *** *****/ /* 基于遺傳算法的人工生命模擬 AL_GA.C */ /* An Artificial Life Simulation model Based on Genetic Algorithm */ /* 同濟大學計算機系 王小平 2000年5月
標簽： Artificial Simulation AL_GA model
上傳時間： 2016-11-15
上傳用戶：ls530720646
％ 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
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