LL(1)文法使用的是確定的自頂向下的分析技術(shù)。LL(1)的含義是：第一個(gè)L表明自頂向下分析是從左向右掃描輸入串，第2個(gè)L表明分析過(guò)程中將使用最左推導(dǎo)，1表明只需向右看一個(gè)符號(hào)便可決定如何推導(dǎo)，即選擇哪個(gè)產(chǎn)生式(規(guī)則)進(jìn)行推導(dǎo)。 LL(1)文法的判別需要依次計(jì)算FIRST集、FOLLOW集和SELLECT集,然后判斷是否為L(zhǎng)L(1)文法,最后再進(jìn)行句子分析。
上傳時(shí)間： 2015-12-26
上傳用戶：kikye
#if !defined(AFX_GAQUEEN_H__C26AE0A3_F9B4_426F_A324_B460CC7946CB__INCLUDED_) #define AFX_GAQUEEN_H__C26AE0A3_F9B4_426F_A324_B460CC7946CB__INCLUDED_ #if _MSC_VER > 1000 #pragma once #endif // _MSC_VER > 1000 class CGAQueen { public: CGAQueen(int nPopulation,int nIteration,float Mutation,int mChBoard) virtual ~CGAQueen() void Clear() // to clear chess board with 0 value void InitialPopulation() // to create the first and initial randompopulation void FillArea(int index) // to fill chess board with desired chromosome int CostFunc(int index) // determine the cost of matrix[index][index] void PopulationSort() // to sort population from the best to the worst void GenerateCrossOverMatrix() // a way to create children from parent is CcrossOver void Mating() // to create children from parents void Ap
標(biāo)簽： AFX_GAQUEEN_H INCLUDED defined define
上傳時(shí)間： 2015-12-27
上傳用戶：wuyuying
We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation, phase-shift keying, and pulse amplitude modulation communications systems.We study the performance of a standard CFO estimate, which consists of first raising the received signal to the Mth power, where M is an integer depending on the type and size of the symbol constellation, and then applying the nonlinear least squares (NLLS) estimation approach. At low signal-to noise ratio (SNR), the NLLS method fails to provide an accurate CFO estimate because of the presence of outliers. In this letter, we derive an approximate closed-form expression for the outlier probability. This enables us to predict the mean-square error (MSE) on CFO estimation for all SNR values. For a given SNR, the new results also give insight into the minimum number of samples required in the CFO estimation procedure, in order to ensure that the MSE on estimation is not significantly affected by the outliers.
標(biāo)簽： frequency-offset estimation quadrature amplitude
上傳時(shí)間： 2014-01-22
上傳用戶：牛布牛
《為Windows Vista編寫安全的代碼》，2007年出版 Providing developers with first-hand insights into design decisions and practical advice for solving real-world security issues, this authoritative guide urges developers to write more secure code on the Vista platform to support a growing customer base. Table of Contents Writing Secure Code for Windows Vista Foreword Introduction Chapter 1 - Code Quality Chapter 2 - User Account Control, Tokens, and Integrity Levels Chapter 3 - Buffer Overrun Defenses Chapter 4 - Networking Defenses Chapter 5 - Creating Secure and Resilient Chapter 6 - Internet Explorer 7 Defenses Chapter 7 - Cryptographic Enhancements Chapter 8 - Authentication and Authorization Chapter 9 - Miscellaneous Defenses and Security-Related Technologies Index List of Figures List of Tables List of Sidebars
上傳時(shí)間： 2016-01-07
上傳用戶：zxc23456789
This book provides a comprehensive introduction to the modern study of computer algorithms. It presents many algorithms and covers them in considerable depth, yet makes their design and analysis accessible to all levels of readers. We have tried to keep explanations elementary without sacrificing depth of coverage or mathematical rigor.
標(biāo)簽： comprehensive introduction algorithms provides
上傳時(shí)間： 2014-11-23
上傳用戶：ynzfm
ICA介紹課件。There has been a wide discussion about the application of Independence Component Analysis (ICA) in Signal Processing, Neural Computation and Finance, first introduced as a novel tool to separate blind sources in a mixed signal. The Basic idea of ICA is to reconstruct from observation sequences the hypothesized independent original sequences
標(biāo)簽： Independence application discussion Component
上傳時(shí)間： 2016-01-12
上傳用戶：AbuGe
This directory contains the memtest 8051 firmware for the Anchor Chips EZ-USB chip. The purpose of this software is to test the different memory segments in the Ez-Usb chip. This example will output the string "good" on the 7 segment LED if the mem test passes, or it will output the first address where the memtest fails.
標(biāo)簽： directory the contains firmware
上傳時(shí)間： 2013-12-16
上傳用戶：gdgzhym
To date tests are still the best solution mankind has found to deliver working software. This book is the sum of four years of research and practice in the testing field. The practice comes from my IT consulting background, first at Octo Technology and then at Pivolis the research comes from my involvement with open source development at night and on weekends.
標(biāo)簽： solution software mankind deliver
上傳時(shí)間： 2014-01-11
上傳用戶：sclyutian
This the second tutorial of the Writing Device Drivers series. There seems to be a lot of interest in the topic, so this article will pick up where the first left off. The main focus of these articles will be to build up little by little the knowledge needed to write device drivers. In this article, we will be building on the same example source code used in part one. In this article, we will expand on that code to include Read functionality, Handle Input/Ouput Controls also known as IOCTLs, and learn a bit more about IRPs.
標(biāo)簽： the interest tutorial Drivers
上傳時(shí)間： 2016-01-28
上傳用戶：lmeeworm
How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
標(biāo)簽： the decision clusters Cluster
上傳時(shí)間： 2013-12-21
上傳用戶：gxmm
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