計(jì)算智能及其應(yīng)用

前言

大學(xué)最重要的功能是向社會(huì)輸送人才，大學(xué)對(duì)于一個(gè)國(guó)家、民族乃至世界的重要性和貢獻(xiàn)度，很大程度上是通過(guò)畢業(yè)生在社會(huì)各領(lǐng)域所取得的成就來(lái)體現(xiàn)的。中國(guó)科學(xué)技術(shù)大學(xué)建校只有短短的五十年，之所以迅速成為享有較高國(guó)際聲譽(yù)的著名大學(xué)之一，主要就是因?yàn)樗囵B(yǎng)出了一大批德才兼?zhèn)涞膬?yōu)秀畢業(yè)生，他們志向高遠(yuǎn)、基礎(chǔ)扎實(shí)、綜合素質(zhì)高、創(chuàng)新能力強(qiáng)，在國(guó)內(nèi)外科技、’經(jīng)濟(jì)、教育等領(lǐng)域做出了杰出的貢獻(xiàn)，為中國(guó)科大贏得了“科技英才的搖籃”的美譽(yù)。2008年9月，胡錦濤總書記為中國(guó)科大建校五十周年發(fā)來(lái)賀信，信中稱贊說(shuō)：半個(gè)世紀(jì)以來(lái)，中國(guó)科學(xué)技術(shù)大學(xué)依托中國(guó)科學(xué)院，按照全院辦校、所系結(jié)合的方針，弘揚(yáng)紅專并進(jìn)、理實(shí)交融的校風(fēng)，努力推進(jìn)教學(xué)和科研工作的改革創(chuàng)新，為黨和國(guó)家培養(yǎng)了一大批科技人才，取得了一系列具有世界先進(jìn)水平的原創(chuàng)性科技成果，為推動(dòng)我國(guó)科教事業(yè)發(fā)展和社會(huì)主義現(xiàn)代化建設(shè)做出了重要貢獻(xiàn)。據(jù)統(tǒng)計(jì)，中國(guó)科大迄今已畢業(yè)的5萬(wàn)人中，已有42人當(dāng)選中國(guó)科學(xué)院和中國(guó)工程院院士，是同期（自1963年以來(lái)）畢業(yè)生中當(dāng)選院士數(shù)最多的高校之一，其中，本科畢業(yè)生中平均每1000人就產(chǎn)生1名院士和七百多名碩士、博士，比例位居全國(guó)高校之首，還有眾多的中青年才俊成為我國(guó)科技、企業(yè)、教育等領(lǐng)域的領(lǐng)軍人物和骨干，在歷年評(píng)選的“中國(guó)青年五四獎(jiǎng)?wù)隆鲍@得者中，作為科技界、科技創(chuàng)新型企業(yè)界青年才俊代表，科大畢業(yè)生已連續(xù)多年榜上有名，獲獎(jiǎng)總?cè)藬?shù)位居全國(guó)高校前列，鮮為人知的是，有數(shù)千名優(yōu)秀畢業(yè)生踏上國(guó)防戰(zhàn)線，為科技強(qiáng)軍做出了重要貢獻(xiàn)，涌現(xiàn)出二十多名科技將軍和一大批國(guó)防科技中堅(jiān)。

內(nèi)容概要

本書闡述計(jì)算智能的理論和相關(guān)的應(yīng)用。重點(diǎn)介紹了如下三個(gè)方面的內(nèi)容：計(jì)算智能的前沿技術(shù)，可以用計(jì)算智能的方法來(lái)解決的前沿問(wèn)題，計(jì)算智能的最新技術(shù)在相關(guān)領(lǐng)域的應(yīng)用。本書可作為信息科學(xué)技術(shù)領(lǐng)域高年級(jí)本科生和研究生的針對(duì)計(jì)算智能的入門教材，也可以供從事科研和技術(shù)開發(fā)的人員參考。IEEE計(jì)算智能協(xié)會(huì)（www．ieee-cis．ors）是該領(lǐng)域重要學(xué)術(shù)組織，并為本書編寫提供很大幫助。

書籍目錄

Preface to the USTC Alumni's SeriesPreface1 Adaptive Particle Filters　1.1  Bayesian Filtering for Dynamic State Estimation 　　 1.1.1  State and Observation Models　　 1.1.2  Bayesian Filtering Method　1.2  Fundamentals of Particle Filters  　　 1.2.1  Sequential Monte Carlo Method　　 1.2.2  Basic Particle Filtering Algorithms　1.3  Challenging Issues in Particle Filtering　　 1.3.1  Unknown or Varying State Model　　 1.3.2  Construction of Proposal Density　　 1.3.3  Determination of Sample Size 　　 1.3.4  Curse of Dimensionality 　1.4  Adaptive Particle Filtering Algorithms　　 1.4.1  Algorithms with Adaptive Sample Size.　　 1.4.2  Algorithms with Adaptive Proposal:Density 　　 1.4.3  Other Related Algorithms　1.5  Summary　References  　Brief Introduction of Authors2 Feature Localization and Shape Indexing for Content　Based Image Retrieval　2.1  Introduction 　2.2  Locales for Feature Localization  　2.3  Search by Object Model　2.4  Shape Indexing and Recognition　2.5  Experimental Results  　　2.5.1  Search Using Locale-based Models 　　2.5.2  Video Locales　　2.5.3  Shape Indexing and Recognition　2.6  Conclusion   References   Brief Introduction of Authors3 BlueGene/L Failure Analysis and Prediction Models　3.1  Introduction  3.2  BlueGene/L Architecture, RAS Event Logs, and Job Logs　　3.2.1  BlueGene/L Architecture　　3.2.2  RAS Event Logs　　3.2.3  Job Logs    3.3  Impact of Failures on Job Executions  3.4  Failure Prediction Based on Failure Characteristics　　3.4.1  Temporal Characteristics　　3.4.2  Spatial Characteristics   3.5  Predicting Failures Using the Occurrence of Non-Fatal Events  3.6  Related Work  3.7  Concluding Remarks and Future Directions  References  Brief Introduction of Authors4 A Neuro-Fuzzy Approach towards Adaptive Intrusion　Tolerant Database Systems　4.1  Overview  4.2  ITDB architecture  4.3  The Need for Adaptivity   4.4  Intelligent Techniques Solutions in Adaptive　ITDB  4.5  Intelligent Techniques Solutions in Adaptive　ITDB　4.6  The Design of Reconfiguration Components  4.7  Performance Metrics for Adaptive ITDB  4.8  Adaptation Criteria  4.9  The Rule-Based Adaptive Controller  4.10 The Neuro-Fuzzy Adaptive Controller  4.11 The collection of training data  4.12 Evaluation Methodology　　4.12.1 Transaction Simulation　　4.12.2 Evaluation Criteria  4.13 Evaluation of NFAC and RBAC Performance  4.14 Conclusion……

章節(jié)摘錄

插圖：The likelihood-based adaptation method is developed for the SIR particle filtering algorithm and it has been applied to the dynamic Bayesian net- works [17] and mobile robot localization applications [10]. In the likelihood- based method, the sample size is determined based on the likelihood of observations; more specifically, at each iteration of particle filtering, particles are generated until the sum of the unnormalized likelihoods （i.e., the unnorrealized importance weights given in Equation （1.14）） exceeds a pre-specified threshold. Thus, in essence, such a method is based on a fixed sum of the un- normalized weights rather than a fixed sample size [17]. The likelihood-based method is easy to implement.The likelihood-based adaptation method may be justified intuitively: if the sample set is well in tune with the observations, each individual importance weight is large and the sample set remains small, which is typically the case in tracking; however, if the observations carry a lot of surprises, as is the case in initial estimation, the individual particle weights are small and the sample set becomes large [11].  Such a method may be viewed as a type of active learning, where the learning algorithm has the ability to ask for more data when necessary [17].  It is well known that the variance of the importance sampler is a function of the mismatch between the proposal density and the target density [25]. The likelihood-based adaptation method directly relates the sample size with this mismatch.  But, unfortunately, such a mismatch is not always an accurate indicator for the necessary number of particles. In addition, in some scenarios, a few particles with very large importance weights may terminate the sampling-process prematurely. Thus, as discussed in [ii], the likelihood-based method may not always provide the proper adaptation that is needed in particle filtering.

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