How to configure Asterisk in a Linux OS, to manage a VoIP Network.
標(biāo)簽： configure Asterisk Network manage
上傳時(shí)間： 2017-09-28
上傳用戶(hù)：a6697238
ı t is about transport layer for Network lessons
標(biāo)簽： transport Network lessons about
上傳時(shí)間： 2017-09-28
上傳用戶(hù)：ljmwh2000
Using Radial Basis Probabilistic Neural Network for Speech Recognition
標(biāo)簽： Probabilistic Recognition Network Radial
上傳時(shí)間： 2014-01-12
上傳用戶(hù)：fxf126@126.com
A routing scheme for content based Networking. Content-based Network is a Network which does nt make use of the source or the destination address to forward a message. This Network is quite different from the traditional Network.
標(biāo)簽： Network Content-based Networking routing
上傳時(shí)間： 2013-11-25
上傳用戶(hù)：lizhen9880
大名鼎鼎的《Network Programming for Windows》教材聽(tīng)說(shuō)過(guò)吧？這就是此書(shū)的配套源碼，希望你喜歡。
標(biāo)簽： Programming Network Windows Source for
上傳時(shí)間： 2016-05-18
上傳用戶(hù)：sz_zhouyl
Cognitive radio has emerged as a promising technology for maximizing the utiliza- tion of the limited radio bandwidth while accommodating the increasing amount of services and applications in wireless Networks. A cognitive radio (CR) transceiver is able to adapt to the dynamic radio environment and the Network parameters to maximize the utilization of the limited radio resources while providing flexibility in wireless access. The key features of a CR transceiver are awareness of the radio envi- ronment (in terms of spectrum usage, power spectral density of transmitted/received signals, wireless protocol signaling) and intelligence.
標(biāo)簽： Cognitive Wireless Network
上傳時(shí)間： 2020-05-26
上傳用戶(hù)：shancjb
Coverage of routing techniques in various wired and wireless Networks is the unique proposition of this book. Routing protocols and algorithms are the brains of any Network. The selection of topics in this book is clear: we attempt to explain routing in its entirety, starting from fundamental concepts, then moving through routing on the Internet, and finally cutting across the recent‐day cellular, ad hoc, and wireless Networks.
標(biāo)簽： Fundamentals Applications Network Routing
上傳時(shí)間： 2020-05-31
上傳用戶(hù)：shancjb
I would like to thank you for purchasing the second edition of “Industrial Network Security,” especially if you are one of the many supporters of the first edition. When the second edition was announced, many people asked me, “why a second edition?” and even more followed that up with, “and why a coauthor?” These ques- tions are harder to answer than you would think.
標(biāo)簽： Industrial Security Network
上傳時(shí)間： 2020-06-07
上傳用戶(hù)：shancjb
This book is based on a Ph.D. research that has been conducted at the Delft University of Technology in the Netherlands in collaboration with Dutch Electricity & Gas Distribution Network Operator, Stedin. This book was written as a result of wider interest that was shown by different industry groups on this topic after its public defence. This inspired the author to modify and publish a practical version of the research for a greater international audience. Therefore, the content of this book is intended for a double audience, on the one hand for those interested in the organisational and management aspects of maintenance.
標(biāo)簽： Maintenance Electricity Risk-Based Network for
上傳時(shí)間： 2020-06-07
上傳用戶(hù)：shancjb
本文擬借助于神經(jīng)網(wǎng)絡(luò)良好的逼近能力，實(shí)現(xiàn)永磁同步電機(jī)的無(wú)位置傳感器控制。 人工神經(jīng)網(wǎng)絡(luò)(Neural Network)可以逼近任意復(fù)雜非線性映射，具有很強(qiáng)的自學(xué)習(xí)自適應(yīng)能力，十分適合于解決復(fù)雜的非線性控制問(wèn)題。其中，BP神經(jīng)網(wǎng)絡(luò)是目前廣泛應(yīng)用的神經(jīng)網(wǎng)絡(luò)之一，得到了較為深入的研究，其結(jié)構(gòu)簡(jiǎn)單，需要離線確定的參數(shù)少、泛化能力強(qiáng)、逼近精度高、實(shí)時(shí)性強(qiáng)，采用BP神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn)永磁同步電機(jī)的調(diào)速控制具有重要意義。 文中提出了基于BP神經(jīng)網(wǎng)絡(luò)的永磁同步電機(jī)自適應(yīng)調(diào)速控制策略，建立了一種包含辨識(shí)網(wǎng)絡(luò)和控制網(wǎng)絡(luò)的雙神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)控制系統(tǒng)。辨識(shí)網(wǎng)絡(luò)在線動(dòng)態(tài)辨識(shí)系統(tǒng)輸出并對(duì)控制網(wǎng)絡(luò)參數(shù)進(jìn)行調(diào)整，控制網(wǎng)絡(luò)與PI控制方法相結(jié)合實(shí)現(xiàn)永磁同步電機(jī)自適應(yīng)轉(zhuǎn)速控制。仿真結(jié)果表明，該系統(tǒng)動(dòng)態(tài)響應(yīng)快、實(shí)時(shí)性較強(qiáng)、精度較高。 文中提出了一種基于混合訓(xùn)練算法的BP神經(jīng)網(wǎng)絡(luò)永磁同步電機(jī)無(wú)位置傳感器控制方法。采用混沌優(yōu)化和梯度下降法相結(jié)合的混合算法對(duì)BP神經(jīng)網(wǎng)絡(luò)進(jìn)行離線訓(xùn)練后，將其用于永磁同步電機(jī)的轉(zhuǎn)子位置角在線估計(jì)。結(jié)果表明，該訓(xùn)練算法可以有效地加快神經(jīng)網(wǎng)絡(luò)收斂速度，且估計(jì)的轉(zhuǎn)子位置角誤差較小、精度較高。 文中建立了以TMS320F2812芯片為核心的永磁同步電機(jī)調(diào)速控制系統(tǒng)，并進(jìn)行了相應(yīng)的軟硬件設(shè)計(jì)，為實(shí)現(xiàn)永磁同步電機(jī)的各種控制策略奠定了實(shí)驗(yàn)基礎(chǔ)。DSP控制系統(tǒng)為神經(jīng)網(wǎng)絡(luò)訓(xùn)練提供樣本，為研究永磁同步電機(jī)的自適應(yīng)調(diào)速控制和轉(zhuǎn)子位置角估計(jì)創(chuàng)造了條件。
標(biāo)簽： BP神經(jīng)網(wǎng)絡(luò) 永磁同步電機(jī) 自適應(yīng)控制
上傳時(shí)間： 2013-05-23
上傳用戶(hù)：1101055045
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