基于进化规划的BP神经网络学习

通过对将传统的BP算法和遗传算法应用到BP神经网络的学习的研究和分析,指出它们存在的缺陷。提出一个改进的进化规划算法,并将其应用于BP神经网络的权值优化。取XOR问题和4奇偶性问题的实验对传统的进化规划算法和改进的进化规划算法进行实验对比。实验结果表明,本文中提出的改进的进化规划算法优于前2个算法。     

An Adaptive Joint Source/Channel Coding Using Error Correcting Arithmetic Codes

An approximately optimal adaptive arithmetic coding (AC) system using a forbidden symbol (FS) over noisy channels was proposed which allows one to jointly and adaptively design the source decoding and channel correcting in a single process, with superior performance compared with traditional separated techniques. The concept of adaptiveness is applied not only to the source model but also to the amount of coding redundancy. In addition, an improved branch metric computing algorithm and a faster sequential searching algorithm compared with the system proposed by Grangetto were proposed. The proposed system is tested in the case of image transmission over the AWGN channel, and compared with traditional separated system in terms of packet error rate and complexity. Both hard and soft decoding were taken into account.     

Improved List Sphere Decoder for Multiple Antenna Systems

An improved list sphere decoder （ILSD） is proposed based on the conventional list sphere decoder （LSD） and the reduced- complexity maximum likelihood sphere-decoding algorithm. Unlike the conventional LSD with fixed initial radius, the ILSD adopts an adaptive radius to accelerate the list cdnstruction. Characterized by low-complexity and radius-insensitivity, the proposed algorithm makes iterative joint detection and decoding more realizable in multiple-antenna systems. Simulation results show that computational savings of ILSD over LSD are more apparent with more transmit antennas or larger constellations, and with no performance degradation. Because the complexity of the ILSD algorithm almost keeps invariant with the increasing of initial radius, the BER performance can be improved by selecting a sufficiently large radius.     

列车通信以太网周期数据时态约束调度方法

针对工业以太网调度方法以报文截止期为唯一约束的局限,提出了一种基于时态约束的列车通信网络周期数据调度表生成方法.首先基于时间触发机制改造列车以太网的通信与消息模型;其次利用网络设备通信参数与数据报文参数,提出周期数据通信的时态约束有向图量化模型;最后设计了基于可满足性模理论的调度表生成算法,求解单播及广播业务的周期数据调度表以验证算法有效性,并在实际列车通信网络的拓扑条件下进行调度性能测试及分析.实验结果表明:该调度算法能够解决部分传统算法不能调度的通信任务,满足更细化的周期数据调度需求并提升调度灵活性;调度性能测试中最大链路负载超过80%,可调度周期数据业务量达到1 000项.      

Construction of rate-compatible (RC) low-density parity-check (LDPC) convolutional codes based on RC-LDPC block codes

In this paper, a family of rate-compatible (RC) low-density parity-check (LDPC) convolutional codes can be obtained from RC-LDPC block codes by graph extension method. The resulted RC-LDPC convolutional codes, which are derived by permuting the matrices of the corresponding RC-LDPC block codes, are systematic and have maximum encoding memory. Simulation results show that the proposed RC-LDPC convolutional codes with belief propagation (BP) decoding collectively offer a steady improvement on performance compared with the block counterparts over the binary-input additive white Gaussian noise channels (BI-AWGNCs).     

基于人工神经网络的湘江最大洪峰流量中、长期预报

将统计相关性分析与模糊方法相结合,识别出影响湘江湘潭站年最大洪峰流量的前期流域降水、大气环流形势等预报因子,通过比较几种改进的BP网络的优缺点,探讨了BP网络建模过程中存在的几个问题,建立了LMBP算法和自适应BP算法相耦合的神经网络中、长期水文预报模型．预报结果表明：预报效果好、精度高,且具有较高的推广和应用价值．     

基于BP神经网络的煤炭企业可持续发展评价

在分析煤炭企业可持续发展评价方法的基础上，建立了煤炭企业可持续发展评价指标体系，构建了基于误差反向传播算法（BP算法）的多层前向神经网络模型．为减小希望输出与实际输出之间的误差，从输出层神经元到输入层神经元逐层反向修正各连接权，并在网络训练学习的过程中不断执行误差反向传播修正，以提高网络对输入模式响应的正确率．通过算例验证了运用BP神经网络评价煤炭企业可持续发展的有效性和准确性．     

基于蚁群优化的前向神经网络

提出了一种新的神经网络学习方法.利用蚁群算法学习神经网络,克服了传统BP算法的不足.同时,针对蚁群算法主要用于组合优化的应用特点,对其进行了改进.将离散的信息素分布矩阵及概率分布矩阵拓展为连续的信息素分布函数和概率分布函数.将搜索的范围扩展到连续区域.使得新算法兼具了蚁群算法的全局快速寻优能力与神经网络的广泛映射能力.通过实例证明了该方法的有效性和快速性.     

IIR数字滤波器设计的搜寻者优化算法

为进一步提高无限冲击响应(IIR)数字滤波器的性能,提出了一种基于搜寻者优化算法(SOA)的IIR数字滤波器设计方法.SOA基于模拟人的随机搜索行为,由利用位置变化评价得到的经验梯度确定搜索方向,由采用简单模糊规则的不确定性推理确定搜索步长,通过搜寻者在搜索空间的位置更新,实现对优化问题的求解.2个典型设计实例的仿真结果表明,与差分进化算法(DE)和3种改进的粒子群算法(PSO)相比,SOA具有较好的全局寻优能力和较快的收敛速度,能有效地应用于IIR数字滤波器的没计.     

U型不完全多目标拆卸线平衡问题建模与优化

针对U型布局所具有的生产柔性强、效率高等优点,结合仅需考虑需求零部件和危害性零部件的实际拆卸过程,提出U型不完全拆卸线平衡问题(U-shaped partial disassembly line balance problem,UPDLBP),以最小化工作站数量、空闲时间均衡指标、拆卸深度和拆卸成本为优化目标建立数学模...     

基于SOGA的VISSIM仿真模型参数标定方法

微观交通仿真模型是对交通系统进行管理、控制和优化的重要试验手段和工具,而微观交通模型的参数标定是确保微观交通仿真模型能真实、直观地反映交通流运行情况的必要前提.针对遗传算法(GA)的不足,提出了基于自适应正交遗传算法(SOGA)的微观交通仿真模型参数标定方法.选取应用较为广泛的VISSIM仿真模型作为基础平台,给出了该优化方法中染色体的编码解码、适应度函数和自适应正交交叉算子的详细设计.最后将算法应用到北京市荣华中路与荣京西街交叉口模型参数标定中,通过与GA算法、正交试验法对比,SOGA算法得到的适应度函数值为19.43,优于其他标定算法的适应度函数值;同时,SOGA算法迭代时间比GA算法少了40.5%,验证了SOGA算法在VISSIM参数标定上的优越性.     

波形顶板-UHPC组合桥面板优化设计

为解决正交异性钢桥面板的疲劳问题,从其根本原因和提高其抗疲劳性能的基本途径出发,提出一种新型波形顶板-UHPC（超高性能混凝土）组合桥面板结构体系.确定影响新型桥面板受力特性的主要参数及其合理取值范围,使用基于BP（back propagation）神经网络的优化设计模型对结构进行优化设计,就所优化的结构尺寸进行疲劳性能测试.研究结果表明:该结构能大幅减少桥面板结构中的几何构型不连续部位数量和焊缝数量,显著提高顶板局部刚度;波形钢板高度、顶部和底部水平段宽度是结构受力性能的重要影响参数;基于BP神经网络的优化设计模型适用于该类桥面板结构的优化设计,最大误差为4.4%;新的结构体系具有良好的疲劳性能,疲劳寿命超过200 a,为正交异性钢桥面板的疲劳问题提供了较好的综合解决方案.      

A New Arithmetic Coding System Combining Source Channel Coding and MAP Decoding

A new arithmetic coding system combining source channel coding and maximum a posteriori decoding were proposed. It combines source coding and error correction tasks into one unified process by introducing an adaptive forbidden symbol. The proposed system achieves fixed length code words by adaptively adjusting the probability of the forbidden symbol and adding tail digits of variable length. The corresponding improved MAP decoding metric was derived. The proposed system can improve the performance. Simulations were performed on AWGN channels with various noise levels by using both hard and soft decision with BPSK modulation.The results show its performance is slightly better than that of our adaptive arithmetic error correcting coding system using a forbidden symbol.     

基于Ant-agent 的自动化码头AGV 控制算法

为解决自动化码头水平运输区存在的自动导引车(AGV)路径冲突和道路死锁问题,提高运输效率,将AGV视为蚂蚁智能体(Ant-agent),设定其携带负反馈机制的信息素进入运输路网. 引入拥挤度及拥挤度阈值q ,建立新的状态转移规则;针对节点冲突和路径拥堵,构建解决机制;提出基于Ant-agent 的AGV控制算法,采用两阶段均匀设计试验法确定算法最优参数组合. 仿真结果表明,与传统动态路径规划算法对比,所提算法在各运输任务量下的避碰性能、解锁性能和运输效率均有较大提高,可有效地解决AGV路径冲突和道路死锁,提高运输效率.     

Fast affine projection algorithm for adaptive noise canceling and its application on the fetal electrocardiogram extraction

Aimed at the problem of adaptive noise canceling (ANC), three implementary algorithms, which are least mean square (LMS) algorithm, recursive least square (RLS) algorithm and fast affine projection (FAP) algorithm, have been researched. The simulations were made for the performance of these algorithms. The extraction of fetal electrocardiogram (FECG) is applied to compare the application effect of the above algorithms. The proposed FAP algorithm has obvious advantages in computational complexity, convergence speed and steadystate error.     

Discretization Algorithm Based on Particle Swarm Optimization and Its Application in Attributes Reduction for Fault Data

In order to increase the fault diagnosis efficiency and make the fault data mining be realized, the decision table containing numerical attributes must be discretized for further calculations. The discernibility matrix-based reduction method depends on whether the numerical attributes can be properly discretized or not. So a discretization algorithm based on particle swarm optimization (PSO) is proposed. Moreover, hybrid weights are adopted in the process of particles evolution. Comparative calculations for certain equipment are completed to demonstrate the effectiveness of the proposed algorithm. The results indicate that the proposed algorithm has better performance than other popular algorithms such as class-attribute interdependence maximization (CAIM) discretization method and entropy-based discretization method.     

Adaptive Identification of Logging Lithology Based on VPSO-ENN Hybrid Algorithm

Particle swarm optimization (PSO) was modified by variation method of particle velocity, and a variation PSO (VPSO) algorithm was proposed to overcome the shortcomings of PSO, such as premature convergence and local optimization. The VPSO algorithm is combined with Elman neural network (ENN) to form a VPSO-ENN hybrid algorithm. Compared with the hybrid algorithm of genetic algorithm (GA) and BP neural network (GA-BP), VPSO-ENN has less adjustable parameters, faster convergence speed and higher identification precision in the numerical experiment. A system for identifying logging parameters was established based on VPSO-ENN. The results of an engineering case indicate that the intelligent identification system is effective in the lithology identification.     

Support vector regression based color image restoration in YUV color space

A support vector regression(SVR) based color image restoration algorithm is proposed.The test color images are firstly mapped into the YUV color space,and then SVR is applied to build up a theoretical model between the degraded images and the original one.Performance comparisons of the proposed algorithm versus traditional filtering algorithms are given.Experimental results show that the proposed algorithm has better performance than traditional filtering algorithms and has less computation time than iterat...     

基于差分模型的无控制DEM差异探测方法

为了提高现有方法的性能，提出了基于差分模型的无控制数字高程模型（DEM）差异探测的新方法，并成功地用于普歪沟泥石流地区地表变形探测（该区变形面积超过50％）．该方法在LZD算法（最小高差法）的基础上引入差分模型，兼顾了观测值的数值及其相互关系的影响，显著提了高算法的差异探测性能．结果表明，新方法的匹配精度比目前代表性的采用肌估计的LZD算法（M-LZD）和采用最小平方中位数的LZD算法（LMSLZD）的精度高．     

An Object-Based Fast Motion Estimation Algorithm in MPEG-4

Introduction The MPEG has successfully developed theMPEG-4 standard[1]to address a wide range ofmultimedia data compression applications. As oneof the major parts of MPEG-4 standard, MPEG-4video aims at providing standardized core technolo-gies allowing efficient storage, transmission andmanipulation of video data in multimedia environ-ments[2]. Compared with its predecessor, MPEG-4 em-ploys many new compression techniques[3]. One ofthe main new features of MPEG-4 is its ability tore…