自適應(yīng)(Adaptive)神經(jīng)網(wǎng)絡(luò)源程序 The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms.~..~ There are 11 blocks that implement basically these 5 kinds of neural Networks: 1) Adaptive Linear Network (ADALINE) 2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm A simulink example regarding the approximation of a scalar nonlinear function of 4 variables
標(biāo)簽： collection implement Adaptive adaptive
上傳時(shí)間： 2015-04-09
上傳用戶：ywqaxiwang
This a project using RBF Neural Network. developed using matlab 7. just load in matlab and can run it.
標(biāo)簽： matlab using developed project
上傳時(shí)間： 2013-12-09
上傳用戶：yuzsu
ACE Programmer s Guide, The: Practical Design Patterns for Network and Systems Programming
標(biāo)簽： Programming Programmer Practical Patterns
上傳時(shí)間： 2015-04-13
上傳用戶：ZJX5201314
This program queries the Network and shows the Domains/Servers/Workstations structure. It also shows the users of each Server or Workstation and can send messages to the selected PC. This programs works only on a Windows NT 4.0 Machine! 查詢網(wǎng)絡(luò),顯示 域/服務(wù)器/工作站的結(jié)構(gòu),它也顯示用戶,并將信息發(fā)送到指定的PC,只能在NT 4上工作
標(biāo)簽： shows Workstations the structure
上傳時(shí)間： 2015-04-13
上傳用戶：sclyutian
* A ncurses user interface. * Network statistics to view the amount of packets and data in many different protocols, interfaces and hosts. * View what active TCP connections are on the Network. * View UDP packets. * View and log ICMP packets. * View and log the 48bit arp protocol. And also view what make of Network card is in each machine * Multithreaded so that the user interface does not interfere with any of the packet captureing methods. * View and log the following user space protocols FTP, POP3, HTTP
標(biāo)簽： statistics interface ncurses Network
上傳時(shí)間： 2015-04-20
上傳用戶：bjgaofei
誤差反向傳播網(wǎng)絡(luò)(Back propagation Network,簡(jiǎn)稱BP網(wǎng)絡(luò))是神經(jīng)網(wǎng)絡(luò)中最活躍的方法，且絕大多數(shù)采用了三層結(jié)構(gòu)(輸入層、一個(gè)隱含層和輸出層).BP網(wǎng)絡(luò)是一種非線性映射人工神經(jīng)網(wǎng)絡(luò).本程序用vb實(shí)現(xiàn)的bp算法
標(biāo)簽： propagation Network Back 誤差
上傳時(shí)間： 2015-04-22
上傳用戶：qiaoyue
一個(gè)老外寫的CMAC Neural Network源代碼和說明
標(biāo)簽： Network Neural CMAC 源代碼
上傳時(shí)間： 2015-04-26
上傳用戶：13215175592
Kismet is an 802.11b Network sniffer and Network dissector. It is capable of sniffing using most wireless cards, automatic Network IP block detection via UDP, ARP, and DHCP packets, Cisco equipment lists via Cisco Discovery Protocol, weak cryptographic packet logging, and Ethereal and tcpdump compatible packet dump files. It also includes the ability to plot detected Networks and estimated Network ranges on downloaded maps or user supplied image files. Kismet是一個(gè)針對(duì)IEEE802.11b無線局域網(wǎng)的嗅探和包分析器，支持大多數(shù)無線網(wǎng)卡，支持自動(dòng)檢測(cè)UDP、ARP和DHCP的數(shù)據(jù)包，支持通過CDP協(xié)議檢測(cè)思科網(wǎng)絡(luò)設(shè)備，支持加密數(shù)據(jù)包記錄，采用與Ethereal和Tcpdump兼容的的數(shù)據(jù)包記錄文件，支持通過用戶提供地圖來檢測(cè)和評(píng)估無線網(wǎng)絡(luò)范圍。
標(biāo)簽： Network dissector sniffing capable
上傳時(shí)間： 2014-11-26
上傳用戶：wweqas
LVQ學(xué)習(xí)矢量化算法源程序 This directory contains code implementing the Learning vector quantization Network. Source code may be found in LVQ.CPP. Sample training data is found in LVQ1.PAT. Sample test data is found in LVQTEST1.TST and LVQTEST2.TST. The LVQ program accepts input consisting of vectors and calculates the LVQ Network weights. If a test set is specified, the winning neuron (class) for each neuron is identified and the Euclidean distance between the pattern and each neuron is reported. Output is directed to the screen.
標(biāo)簽： implementing quantization directory Learning
上傳時(shí)間： 2015-05-02
上傳用戶：hewenzhi
本程序用資源分配網(wǎng)(Resource_Allocation Network,簡(jiǎn)稱RAN)實(shí)現(xiàn)了Hermit多項(xiàng)式在線學(xué)習(xí)問題。訓(xùn)練樣本產(chǎn)生方式如下，樣本數(shù)400，每個(gè)樣本輸入Xi在區(qū)間[-4,4]內(nèi)隨機(jī)產(chǎn)生（均勻分布），相關(guān)樣本輸出為F(Xi) = 1.1(1-Xi + Xi2)exp(-Xi2/2),測(cè)試樣本輸入在[-4，+4]內(nèi)以0.04為間隔等距產(chǎn)生，共201個(gè)樣本。訓(xùn)練結(jié)束后的隱節(jié)點(diǎn)為：11個(gè)，訓(xùn)練結(jié)束后的平均誤差可達(dá)：0.03
標(biāo)簽： Resource_Allocation Network Hermit RAN
上傳時(shí)間： 2014-01-14
上傳用戶：pompey
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