SpikeLM: A Second-Order Supervised Learning Algorithm for Training Spiking Neural Networks Introduction Preliminaries SpikeLM algorithm Experimental validation Conclusions
標(biāo)簽： Second-Order Supervised Algorithm Learning
上傳時間： 2014-01-13
上傳用戶：zhuimenghuadie
We shall revisit the limitations of the two-layer networks of the previous one.
標(biāo)簽： the limitations two-layer networks
上傳時間： 2016-02-27
上傳用戶：shus521
這程序是《神經(jīng)網(wǎng)絡(luò)模式識別及其實現(xiàn)Networks in C++)美Abhijit S. Pandya等著的HOPFIELD源代碼.
標(biāo)簽： S. Networks HOPFIELD Abhijit
上傳時間： 2014-01-15
上傳用戶：jcljkh
EM for neural networks
標(biāo)簽： networks neural for EM
上傳時間： 2016-03-03
上傳用戶：banyou
Detailed OFDM Modeling in Network Simulation of Mobile Ad Hoc Networks*
標(biāo)簽： Simulation Detailed Modeling Networks
上傳時間： 2013-12-02
上傳用戶：253189838
學(xué)習(xí)mp3格式的好源碼A DSP-based decompressor unit for high-fidelity MPEG-Audio over TCP/IP networks
標(biāo)簽： high-fidelity decompressor MPEG-Audio DSP-based
上傳時間： 2016-04-03
上傳用戶：gaome
Offers sound advice on selecting a thesis or postdoctoral adviser, choosing among research jobs in academia, government laboratories, and industry, preparing for an employment interview, and defining a research program.
標(biāo)簽： postdoctoral selecting choosing research
上傳時間： 2014-01-20
上傳用戶：kelimu
Fuzzy logic based handoff in wireless networks.rar
標(biāo)簽： wireless networks handoff Fuzzy
上傳時間： 2014-01-16
上傳用戶：liansi
This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標(biāo)簽： reversible algorithm the nstrates
上傳時間： 2014-01-08
上傳用戶：cuibaigao
2005 Wiley)Topology Control in Wireless Ad-Hoc and Sensor Networks.pdf
標(biāo)簽： Topology Networks Wireless Control
上傳時間： 2016-04-27
上傳用戶：ghostparker
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