认知OFDMA混合网络模型中基于QoE的资源分配协议(英文)

提出了一种应用于基于正交频分多址接入(OFDMA)的认知网络模型中的资源分配协议,其认知网络采用了融合overlay和underlay的混合网络模型.在不影响主用户通信质量的前提下,overlay模型允许认知用户利用主用户未使用的空闲子信道;underlay模型允许认知用户和主用户共同占用子信道.该协议旨在最大化认知用户的体验质量(Qo E),动态地在overlay和underlay模型之间切换,并通过平均意见分数(MOS)函数进行量化,而非简单地满足各个用户在物理层和MAC层的限制条件.从对认知用户的文件传输和视频服务的仿真结果可看出,所提出的资源分配协议具有较高的频谱效率.     

高速铁路下行MIMO-OFDM系统中的动态资源分配策略(英文)

对高速铁路下行MIMO-OFDM系统中的动态资源分配问题进行研究.联合考虑子载波、天线、时隙和功率,将多维资源分配问题建模为混合整数非线性规划问题.分析移动速度对多普勒频移的影响,并计算子载波间干扰功率.在总发射功率不超过一定阈值的约束条件下,将最大化系统吞吐量作为优化目标.为了降低求解最优化问题的计算复杂度,采用两步求解法得到次优解.首先,在等功率分配的前提下,将子载波、天线和时隙分配给不同用户.然后,根据第1步资源分配的结果,进行功率分配.仿真结果显示,提出的多维资源分配策略与已有策略相比在系统吞吐量方面具有较大的性能提高.     

下行多用户MC-CDMA系统中的自适应资源分配

研究了多用户场景下多载波码分多址系统（MC—CDMA）的下行信道和功率分配,并将吞吐最大化问题建模成一个混合整数优化问题．为了简化分析,将问题分成2个低复杂度的子问题：功率分配和信道分配．这2个子问题可分别被一个次最优自适应功率分配算法（APA）和一个最优自适应信道分配算法（ACA）解决．通过联合APA和ACA算法,进一步提出了一个自适应信道和功率的分配方案．仿真结果表明：与传统的均匀功率分配算法相比,提出的APA算法更加适用于MC—CDMA系统;此外,提出的自适应信道和功率分配方案可以显著地提高系统吞吐量性能．     

Optimal distributed resource allocation in a wireless sensor network for control systems

INTRODUCTION With the rapid development of wireless com-munication technology, the combination of wireless communication and control systems becomes a new trend of networked control systems. Among the kinds of wireless technologies, wireless sensor network (WSN) has attracted a lot of interest and visibility due to its huge application space. WSN is a kind of wire-less ad-hoc network which connects embedded sen-sors, actuators, and processors and in which each node consists of a wireles…     

基于模糊神经网络PID的无刷直流电机控制系统研究

针对传统PID控制在无刷直流电机控制系统中达不到良好的控制效果的问题,在无刷直流电机的数学模型基础之上,设计一种模糊神经网络自适应PID控制器,该控制器利用模糊控制非线性控制作用和BP神经网络的学习能力及适应能力相结合对PID参数进行在线实时调整。对基于模糊神经网络自适应PID控制器的无刷直流电机的双闭环控制系统进行仿真实验,实验结果表明,可以提高控制系统的响应速度,减小超调量,对负载及电机参数的变化都有较强的鲁棒性。     

增强现实系统中基于神经网络的磁力跟踪器校正

Nonlinear errors always exist in data obtained from tracker in augmented reality （AR）, which badly influence the effect of AR. This paper proposes to rectify the errors using BP neural network. As BP neural network is prone to getting into local extrema and convergence is slow, genetic algorithm is employed to optimize the initial weights and threshold of neural network. This paper discusses how to set the crucial parameters in the algorithm. Experimental results show that the method ensures that the neural network achieves global convergence quickly and correctly. Tracking precision of AR system is improved after the tracker is rectified, and the third dimension of AR system is enhanced.     

Interval standard neural network models for nonlinear systems

INTRODUCTION Neural networks have been successfully em- ployed for controlling nonlinear systems since the 1990’s (Narendra and Parthasarathy 1990; Hunt et al., 1992; Suykens et al., 1996). In these nonlinear control systems, neural networks have been used either for modelling the system to be controlled, or for design- ing a controller, or both. Recently, the robustness issue has been an important focus of research in neuro-control circles (Suykens et al., 1996; Wams et al., 1999; Aya…     

Rectification of magnetic force tracker using neural network inaugmented reality system

1IntroductionAugmented reality(AR)is a newtechnique based onvirtual reality,which has attracted much attention inrecent years.AR is used to describe a system thatenhances the real world by superi mposing computer-generated information on top of it.It supp…     

A new neural network model for the feedback stabilization of nonlinear systems

A new neural network model termed ＇standard neural network model＇ （SNNM） is presented, and a state-feedback control law is then designed for the SNNM to stabilize the closed-loop system. The control design constraints are shown to be a set of linear matrix inequalities （LMIs）, which can be easily solved by the MATLAB LMI Control Toolbox to determine the control law. Most recurrent neural networks （including the chaotic neural network） and nonlinear systems modeled by neural networks or Takagi and Sugeno （T-S） fuzzy models can be transformed into the SNNMs to be stabilization controllers synthesized in the framework of a unified SNNM. Finally, three numerical examples are provided to illustrate the design developed in this paper.     

A hybrid neural network system for prediction and recognition of promoter regions in human genome

INTRODUCTION The publication and preliminary analysis of thehuman genome sequence (Lander et al., 2001; Venteret al., 2001) marks a significant milestone in the fieldof molecular biology. One of the main goals of theHuman Genome Project is the characterization, an-notation?recognition and categorization of genesfrom human genome to serve as a periodic table forbiomedical research (Lander, 1996). In the past fewyears, many efforts have been devoted to gene anno-tations. The Nation…     

An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash

In this study, an artificial neural network （ANN） model for studying the strength properties of steel fiber reinforced concrete （SFRC） containing fly ash was devised. The mixtures were prepared with 0 wt%, 15 wt%, and 30 wt% of fly ash, at 0 vol.%, 0.5 vol.%, 1.0 vol.% and 1.5 vol.% of fiber, respectively. After being cured under the standard conditions for 7, 28, 90 and 365 d, the specimens of each mixture were tested to determine the corresponding compressive and flexural strengths. The pa- rameters such as the amounts of cement, fly ash replacement, sand, gravel, steel fiber, and the age of samples were selected as input variables, while the compressive and flexural strengths of the concrete were chosen as the output variables. The back propagation learning algorithm with three different variants, namely the Levenberg-Marquardt （LM）, scaled conjugate gradient （SCG） and Fletcher-Powell conjugate gradient （CGF） algorithms were used in the network so that the best approach can be found. The results obtained from the model and the experiments were compared, and it was found that the suitable algorithm is the LM algorithm. Furthermore, the analysis of variance （ANOVA） method was used to determine how importantly the experimental parameters affect the strength of these mixtures.