非最小相位线性系统的自适应建模

自适应逆控制（Adaptive Inverse Control）把自适应滤波器技术较好地应用到控制领域中,较传统控制方法有诸多优点和研究价值。对非最小相位对象的自适应逆控制,一般需要先进行稳定性分析,保证其稳定性前提下进行控制。课题着重研究了非最小相位线性系统的自适应建模这一环节。首先介绍了自适应滤波器理论的相关知识,接下来讨论自适应控制的建模原理,并完成线性非最小相位对象的自适应建模,最后进行计算机仿真,演示实验结装。     

自适应线性神经网络在车辆组合导航的应用

针对车辆GPS/DR组合导航系统中位置误差预测难以建立精确的数学模型的问题,提出了自适应线性神经网络建立位置误差预测模型的方法。该方法对来自GPS、DR的位置数据采用小波去噪,在GPS有效时用GPS、DR的位置信号建立线性神经网络位置误差预测模型,在GPS失效时预测位置误差并推算车辆实时位置。仿真表明,该方法对车辆组合导航系统是有效的。     

MIMO-OFDM系统基于线性预编码的自适应加载方案

在MIMO-OFDM无线通信系统中,采用基于子载波线性预编码设计和变步长加载算法得到一种优化的自适应加载算法,在完全已知信道状态前提下,能在满足固定速率和给定误比特率约束下使系统总发射功率达到最小。仿真结果表明,该算法复杂度比Capello算法低,并且性能与之近似,都远远优于非自适应MIMIO系统的性能。     

The Modeling of Adaptive Routing in Data-Communication Networks

Basic analytical models for problems of dynamic and quasi-static routing in data-communication networks are introduced. The models are intended to handle the quantities of interest in an algorithmic form, and as such require only a minimal number of assumptions. Control and estimation methods are used to construct algorithms for the solution of the routing problem.     

ARLNCStream:自适应随机网络编码流媒体系统

该文设计了一种自适应随机线性网络编码P2P实时媒体分发系统。提出了一种网状网络拓扑下的编码窗自适应随机线性网络编码算法。仿真结果表明,该算法具有很好的效果：对网络带宽要求低;无需全局网络拓扑信息;平均延迟小,连续性好;鲁棒,可扩展性强。     

自适应OFDMA系统无线资源管理的CPN建模与仿真

基于自适应OFDMA系统,对多业务系统中基于总发射功率最小化的无线资源管理问题进行研究,并使用着色Petri网(CPN,coloured Petri nets)以及CPN分析工具CPN Tools对其进行建模。采用Monitor监控器对模型进行仿真分析,并将仿真结果与随机Petri网(SPN,stochastic Petri nets)模型以及基于吞吐量最大化的CPN模型进行比较,验证了该系统模型的正确性和优越性。     

Adaptive Methods for Linear Programming Decoding

Detectability of failures of linear programming (LP) decoding and the potential for improvement by adding new constraints motivate the use of an adaptive approach in selecting the constraints for the underlying LP problem. In this paper, we make a first step in studying this method, and show that by starting from a simple LP problem and adaptively adding the necessary constraints, the complexity of LP decoding can be significantly reduced. In particular, we observe that with adaptive LP decoding, the sizes of the LP problems that need to be solved become practically independent of the density of the parity-check matrix. We further show that adaptively adding extra constraints, such as constraints based on redundant parity checks, can provide large gains in the performance.      

Constrained Adaptive Linear Multiuser Detection Schemes

By using a fair comparison method we show that contrary to the general belief the conventional LMS, when in training mode, does not necessarily outperform the popular blind LMS (BLMS). With the help of a constrained MMSE criterion we identify the correct trained version which is guaranteed to have uniformly superior performance over BLMS since it maximizes the SIR over an algorithmic class containing BLMS. Because the proposed optimum trained version requires knowledge of the amplitude of the user of interest we also present simple and efficient techniques that estimate the amplitude in question. The resulting algorithm in both modes, training and decision directed, is significantly superior to BLMS.     

Minimum Bayes Risk Adaptive Linear Equalizers

This paper introduces Bayes risk (expected loss) as a criterion for linear equalization. Since the probability of error is equal to the Bayes risk (BR) for a particular binary loss function, this work is a natural generalization of previous works on minimum probability of error (PE) equalizers. Adaptive equalization algorithms are developed that minimize the BR. Like the minimum PE equalizers, the BR algorithms have low computational complexity which is comparable to that of the LMS algorithm. The advantage of the BR criterion is that the loss function can be specified in a manner that accelerates adaptive equalizer convergence relative to the minimum PE adaptive algorithm as illustrated in simulation examples. Besides introducing a new criterion, this paper provides another independent contribution to the field of PE minimizing equalization. While most prior works focus on $M$-ary QAM type modulations with rectangular decision regions, this paper uses upper bounds on the probabilities of certain events to yield tractable mathematics that apply to two-dimensional constellations with arbitrarily shaped decision regions. The resulting adaptive algorithm use the full information available in the phase of the error signal, whereas previous algorithms use a quantized version of this error phase.      

一种语音信号的线性局部自适应递推预测算法

语音产生过程包含非线性过程,传统的线性预测方法不能很好地解决这些非线性成份。局部线性预测是一种高精度的预测算法,但计算复杂度较大。为提高非线性预测的速度,提出了一种自适应递推局部线性预测算法．并设计算法的步骤,分析算法的复杂性。仿真结果表明,该算法比线性预测算法精度高,是一种有效的语音信号非线性预测方法。     

自适应前馈射频功率放大器设计

在高功率射频放大器设计时，通常采用前馈技术来改善宽带信号的线性度。本文基于普通的前馈系统。提出了一种新的自适应前馈线性化技术，并结合信号包络检测技术进行带外信号调节。同时，还利用该技术在计算机仿真系统上设计了一个射频功率放大器的功放输出级，通过对仿真结果的比较，发现他能显著改善IMD性能。     

基于功率调度的在线建模自适应反馈控制系统的研究

自适应有源噪声控制是噪声控制中的一项先进技术。使用附加噪声建模的自适应控制系统会增加输出的残留噪声,而且还可能造成反馈控制系统发散。功率调度方法可以有效地减小残留噪声,增加系统的鲁棒性。根据是否使用功率调度方法,对2种在线次路径建模自适应反馈控制系统进行了研究,仿真结果表明采用功率调度方法的自适应反馈系统比不使用功率调度方法的反馈系统具有更好的降噪效果。     

基于MMSE准则的线性疏散码设计及其在自适应MIMO系统中的应用

线性疏散码（LDC）是一组扩散矩阵的线性组合，加权因子是要发送符号的实部和虚部，好的LDC可达到线性分组码的满分集度性能，同时接近V—BLAST的满信道容量性能。本文提出了设计最优线性疏散码（LDC）的最小均方误差（MMSE）准则，并针对自适应MIMO系统根据信道统计信息自动改变系统参数和确定预编码矩阵的情况，提出了确定线性疏散码编码矩阵中信息符号个数的方法。由上，线性疏散码提高了自适应MIMO系统的1—3dB的误比特率性能。本文使用梯度下降法和坐标转换法解决了最优化问题。     

A Micropower Adaptive Linear Transform Vector Quantiser

Micropower signal classification and compression are becoming important requirements for implantable cardioverter defibrillators although they are currently limited by power and computational constraints. This paper describes an integrated circuit that facilitates Intracardiac Electrogram (ICEG) classification and compression of 30 dimensional analogue vectors while consuming a maximum of 2.5 W power for a heart rate of 60 beats per minute (1 vector per second) from a 3.3 V supply. This represents a significant advance on previous work which achieved ultra low power supervised morphology classification [6] since the templated matching scheme used in this chip enables unsupervised blind classification of abnormal rhythms and the computational support for low bit rate data compression. The adaptive template matching scheme used is tolerant to amplitude variations, and inter- and intra-sample time shifts. Micropower performance is achieved using CMOS analogue circuits biased in weak inversion in order to minimise energy per computation. Results from the fabricated chip demonstrate the impact of the amplitude and shift tolerance on ICEG data and performance for blind classification of an abnormal rhythm in five heart patients. For four out of the five patients, no false negative classifications and a worst case of 11% false positive classifications were made.     

四元数域对合增广宽线性自适应波束形成

该文提出一种基于4元数的宽线性自适应波束形成方法,该方法在利用4元数构建单个阵元输出的基础上,通过充分挖掘4元数的3种对合信息,联合信号的非圆特性,建立4元数域对合增广信号模型,进而实现4元数域宽线性自适应波束形成。同传统4元数波束形成技术相比,该方法对非圆信号的接收性能显著提升,同时可以实现阵列虚拟孔径扩展,增大自适应波束形成干扰抑制自由度。仿真实验验证了所提波束形成方法的性能。     

Adaptive Linear Predictor Tracks Implanted Radiopaque Markers

Radiopaque markers implanted in the heart's wall permit one to observe absolute motion of the heart in closed-chest animals and humans using X-rays. The resulting data, however, are very tedious to analyze because human operators must digitize the observed marker motion from cineradiographic films (or stored video images). This paper describes an algorithm that tracks the trajectories of markers implanted in the endocardium. The algorithm uses a least-mean-squares adaptive linear predictor. By solving the marker tracking problem, one moves closer to a fully automated endocardial marker data acquisition system. The algorithm is also used to detect errors in data acquired using manual digitizing procedures. Tests run with data from nine dogs showed that the algorithm could successfully track and identify 95?100 percent of the markers in 40 out of the 60 film segments and 90?100 percent of the markers in 52 out of 60. The root-mean-square distance between the identified marker trajectories and the manually determined trajectories, a measure of accuracy, was about one-half the marker size when averaged over all of the film segments tested.     

Modeling and Simulation in Support of System Level Design for High Acceleration Linear Motors

Software tools offer powerful support in the areas of engineering specification, design, implementation, and test. The tools are at their most potent when they actively promote agility and responsiveness throughout a product life cycle and leave a legacy of knowledge to inform future product development. Model-based design facilitates these benefits by considering a simulation of the system under development as an executable specification. This executable specification may be regarded as “one truth” across engineering teams with the simulation being abstracted or enhanced as appropriate. First-principle, data-driven, and physical modeling further strengthens model-based design, by allowing the agility and responsiveness afforded by model-based design to be relevant for both algorithmic and nonalgorithmic design considerations. Indeed, models are a powerful means to offer support for in-service operation, diagnostics of unintended operations and assessment and upgrades of control systems and/or system architectures during the entire life-cycle of a product. This paper will consider the benefits of physical modeling and model-based design through an example of a high acceleration linear motor. The motor type, power electronic-drive switching strategy, and power-electronic drive architecture will be considered. Finally, the use of parallel computing within the context of this application will be discussed, in particular as an effective means to generate results for a large number of operational scenarios in a time-effective manner.      

The Adaptive Ontology-Based Personalized Recommender System

Recommender systems provide strategies that help users search or make decisions within the overwhelming information spaces nowadays. They have played an important role in various areas such as e-commerce and e-learning. In this paper, we propose a hybrid recommendation strategy of content-based and knowledge-based methods that are flexible for any field to apply. By analyzing the past rating records of every user, the system learns the user’s preferences. After acquiring users’ preferences, the semantic search-and-discovery procedure takes place starting from a highly rated item. For every found item, the system evaluates the Interest Intensity indicating to what degree the user might like it. Recommender systems train a personalized estimating module using a genetic algorithm for each user, and the personalized estimating model helps improve the precision of the estimated scores. With the recommendation strategies and personalization strategies, users may have better recommendations that are closer to their preferences. In the latter part of this paper, a real-world case, a movie-recommender system adopting proposed recommendation strategies, is implemented.     

线性约束差分恒模自适应阵列性能分析

线性约束差分恒模算法(LCDCM)能有效克服恒模算法(CMA)中存在的干扰捕获问题,但其性能受迭代步长的影响。本文对所需信号功率、干扰信号功率及所需信号和干扰信号空域相关系数对迭代步长取值和算法收敛速度的影响进行了分析,给出了保证算法收敛的迭代步长的取值范围。最后利用计算机仿真对上述理论结果进行了验证。