We shall revisit the limitations of the two-layer networks of the previous one.
標簽： the limitations two-layer networks
上傳時間： 2016-02-27
上傳用戶：shus521
％ 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
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
In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
標簽： Rauch-Tung-Striebel algorithm smoother which
上傳時間： 2016-04-15
上傳用戶：zhenyushaw
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.
標簽： Single-layer algorithms best-known networks
上傳時間： 2015-06-17
上傳用戶：趙云興
外國人開發的電磁時域有限差分方法工具包 Electromagnetic Finite-Difference Time-Domain (EmFDTD) is a basic two-dimensional FDTD code developed at the School of Electrical Engineering, Sharif University of Technology. This code has been written based on the standard Yee s FDTD algorithm. Applications include propagation, scattering, and diffraction of electromagnetic waves in homogeneous and non-homogeneous isotropic media for in-plane propagating waves. Negative permittivites or permeabilities as well as dispersion is not included. Zero, Periodic, and Perfectly Matched Layer boundary conditions may be selectively applied to the solution domain. The program is best suited for study of propagation and diffraction of electromagnetic waves in Photonic Crystal structures. EmFDTD is written in MATLAB language and has been tested under MATLAB 5.0 and higher versions.
標簽： Finite-Difference Electromagnetic two-dimensio Time-Domain
上傳時間： 2014-11-24
上傳用戶：watch100
Performance Comparison of Two On-Demand Routing Protocols Depends on Traffic Density by Using UWB-IR as Pyhisical and MAC Layer at Outdoor Peer to Peer Sensor Network
標簽： Performance Comparison On-Demand Protocols
上傳時間： 2017-05-28
上傳用戶：三人用菜
FPGA-based link layer chip S19202 configuration
標簽： configuration FPGA-based S19202 layer
上傳時間： 2013-08-18
上傳用戶：xsnjzljj
This document provides practical, common guidelines for incorporating PCI Express interconnect layouts onto Printed Circuit Boards (PCB) ranging from 4-layer desktop baseboard designs to 10- layer or more server baseboard designs. Guidelines and constraints in this document are intended for use on both baseboard and add-in card PCB designs. This includes interconnects between PCI Express devices located on the same baseboard (chip-to-chip routing) and interconnects between a PCI Express device located “down” on the baseboard and a device located “up” on an add-in card attached through a connector. This document is intended to cover all major components of the physical interconnect including design guidelines for the PCB traces, vias and AC coupling capacitors, as well as add-in card edge-finger and connector considerations. The intent of the guidelines and examples is to help ensure that good high-speed signal design practices are used and that the timing/jitter and loss/attenuation budgets can also be met from end-to-end across the PCI Express interconnect. However, while general physical guidelines and suggestions are given, they may not necessarily guarantee adequate performance of the interconnect for all layouts and implementations. Therefore, designers should consider modeling and simulation of the interconnect in order to ensure compliance to all applicable specifications. The document is composed of two main sections. The first section provides an overview of general topology and interconnect guidelines. The second section concentrates on physical layout constraints where bulleted items at the beginning of a topic highlight important constraints, while the narrative that follows offers additional insight.
上傳時間： 2013-10-15
上傳用戶：busterman
L2TP（Layer Two tunneling Protocol）是實現二層隧道VPN（Virtual Private Network）的主要技術，有L2TPV2和L2TPV3兩個協議標準；為了進一步提高私有網絡的安全性，本文研究了VPN和L2TP隧道技術的基本實現技術，并在現有L2TPV2協議非對稱工作模型的基礎上實現了向下兼容的支持對稱工作模型的L2TPV3協議，并對系統的功能和性能進行了測試和分析，測試結果顯示該實現方案能夠正常完成L2TPV3隧道的建立以及協議報文的收發，且系統性能穩定。
標簽： L2TPV3
上傳時間： 2013-10-31
上傳用戶：iven
