A gradual neural-network approach for frequency assignment insatellite communication systems

A novel neural-network approach called gradual neural network (GNN) is presented for a class of combinatorial optimization problems of requiring the constraint satisfaction and the goal function optimization simultaneously. The frequency assignment problem in the satellite communication system is efficiently solved by GNN as the typical problem of this class. The goal of this NP-complete problem is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate the increasing demands. The GNN consists of NxM binary neurons for the N-carrier-M-segment system with the gradual expansion scheme of activated neurons. The binary neural network achieves the constrain satisfaction with the help of heuristic methods, whereas the gradual expansion scheme seeks the cost optimization. The capability of GNN is demonstrated through solving 15 instances in practical size systems, where GNN can find far better solutions than the existing algorithm.     

A gradual neural-network algorithm for jointly time-slot/code assignment problems in packet radio networks

A gradual neural network (GNN) algorithm is presented for the jointly time-slot/code assignment problem (JTCAP) in a packet radio network in this paper. The goal of this newly defined problem is to find a simultaneous assignment of a time-slot and a code to each communication link, whereas time-slots and codes have been independently assigned in existing algorithms. A time/code division multiple access protocol is adopted for conflict-free communications, where packets are transmitted in repetition of fixed-length time-slots with specific codes. GNN seeks the time-slot/code assignment with the minimum number of time-slots subject to two constraints: (1) the number of codes must not exceed its upper limit and (2) any couple of links within conflict distance must not be assigned to the same time-slot/code pair. The restricted problem for only one code is known to be NP-complete. The performance of GNN is verified through solving 3000 instances with 100-500 nodes and 100-1000 links. The comparison with the lower bound and a greedy algorithm shows the superiority of GNN in terms of the solution quality with the comparable computation time.     

A gradual noisy chaotic neural network for solving the broadcast scheduling problem in packet radio networks

In this paper, we propose a gradual noisy chaotic neural network (G-NCNN) to solve the NP-complete broadcast scheduling problem (BSP) in packet radio networks. The objective of the BSP is to design an optimal time-division multiple-access (TDMA) frame structure with minimal TDMA frame length and maximal channel utilization. A two-phase optimization is adopted to achieve the two objectives with two different energy functions, so that the G-NCNN not only finds the minimum TDMA frame length but also maximizes the total node transmissions. In the first phase, we propose a G-NCNN which combines the noisy chaotic neural network (NCNN) and the gradual expansion scheme to find a minimal TDMA frame length. In the second phase, the NCNN is used to find maximal node transmissions in the TDMA frame obtained in the first phase. The performance is evaluated through several benchmark examples and 600 randomly generated instances. The results show that the G-NCNN outperforms previous approaches, such as mean field annealing, a hybrid Hopfield network-genetic algorithm, the sequential vertex coloring algorithm, and the gradual neural network.     

通道布线的神经网络优化算法

介绍通道布线的思想，给出相应的形式化描述，提出一种神经网络求解算法，该算法以总线长最短，轨道数最少为优化目标，以满足水平，垂直制约为约束条件，通过把问题映射为神经网络模型，建立了问题的能量函数，用均场退火方程迭代求解，实验结果是令人满意的。     

Meta-Heuristic Algorithms for FPGA Segmented Channel Routing Problems with Non-standard Cost Functions

In this paper we present three meta-heuristic approaches for FPGA segmented channel routing problems (FSCRPs) with a new cost function in which the cost of each assignment is not known in advance, and the cost of a solution only can be obtained from entire feasible assignments. Previous approaches to FSCPs cannot be applied to this kind of cost functions, and meta-heuristics are a good option to tackle the problem. We present two hybrid algorithms which use a Hopfield neural network to solve the problem's constraints, mixed with a Genetic Algorithm (GA) and a Simulated Annealing (SA). The third approach is a GA which manages the problem's constraints with a penalty function. We provide a complete analysis of the three metaheuristics, by tested them in several FSCRP instances, and comparing their performance and suitability to solve the FSCRP. This work has been partially supported by a research project of the Universidad de Alcalá, project number UAH PI2005/078.     

基于人工神经网络实现的QoS路由决策

讨论了基于神经网络自学习算法实现QoS路由决策的问题。为了证明利用人工神经网络优化路由决策的可行性,在由17台服务器（节点）搭建的实验网络环境中,每个节点上均设计了由几个神经元组成的神经网络,各神经元依据网络的测量数据,通过学习算法动态地进行路由决策。实验结果表明,在以最小跳转数或最小延时为QoS目标时,神经网络所提供的路由决策均可以有效地使QoS接近最优值;同时,当神经网络综合考虑延时和最小跳转数两项QoS指标时,网络延时状况要优于只考虑一项指标的情况。实验结果证明了利用神经网络在节点上进行分布式的路由     

Lattice Dynamical Wavelet Neural Networks Implemented Using Particle Swarm Optimization for Spatio–Temporal System Identification

In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework.     

无线传感器网络中分段贪婪地理路由算法

针对基于地理位置的无线传感器网络路由中存在的路由空洞问题,提出一种新的路由模式:分段贪婪路由.在该模式中,整个路由过程被中间节点序列划分为若干段,在每一段上仅应用贪婪转发策略.为确定合适的中间节点,给出一种基于递归探测的方法,并以GPSR算法为基础探测路由构造了SGR算法.仿真实验表明,在存在不同类型、大小、数量路由空洞的网络环境中,SGR算法均能以较小的探测开销获得接近最优的路由路径,尤其是凹空洞存在的情况.     

知识图谱增强的图神经网络推荐研究进展

已有推荐方法主要基于用户与项目的历史交互行为,未充分运用用户及项目相关特征信息,推荐效果并不理想。知识图谱（knowledge graph,KG）增强的图神经网络（graph neural network,GNN）推荐,是以用户与项目交互行为构建的交互图为基础,引入同为图结构的知识图谱,并运用图神经网络技术进行处理,从而实现个性化推荐。深入探讨了现有知识图谱增强的图神经网络推荐研究进展。首先在对图神经网络推荐和知识图谱推荐进行探讨的基础上,从项目知识图谱和协同知识图谱视角,深入分析了当前知识图谱增强的图神经网络推荐取得的相关研究成果;然后从大规模动态知识图谱处理、用户对项目属性的偏好挖掘、知识图谱的图嵌入学习等方面,指出了已有知识图谱增强的图神经网络推荐研究存在的主要问题;最后从动态时序知识图谱增强的GNN推荐、元学习的知识图谱增强GNN推荐、多模态知识图谱增强的GNN推荐、知识图谱增强的GNN跨领域推荐等方面,展望了知识图谱增强的图神经网络推荐未来主要研究方向。     

基于混沌神经网络的最短路径路由算法

将最短路径问题映射到混沌神经网络，提出了一种带有混沌噪音的神经网络最短路径路由算法。首先设计了与最短路径有关的网络费用和路径表达方法；其次结合混沌神经网络的数学模型建立神经元的运动方程；最后依据网络费用和约束条件构造神经网络的能量函数。分别在具有9个结点和15个结点的网络拓扑结构上进行了实验，单个和多个分组请求均能快速地找到最短路径。结果表明，该文提出的最短路径路由算法用于高速交换网络是有效可行的。     

An on-demand routing protocol for improving channel use efficiency in multichannel ad hoc networks

In wireless ad hoc networks, end-to-end delivery over network is a critical concern for routing protocols. The capacity of routing protocols is constrained by the intra-flow interference introduced by adjacent nodes on the same path, and inter-flow interference generated by nodes from neighboring paths. In this paper, we develop an on-demand routing protocol M-AODV-R that solves the channel assignment, reuse and routing problem jointly. The proposed channel reuse scheme and channel assignment scheme can enhance channel reuse rate. This cross-layer design approach can significantly improve the performance of multichannel ad hoc networks over existing routing protocols. Simulation results show that the proposed routing M-AODV-R can effectively increase throughput and reduce delay, as compared to AODV protocol.     

Valiant load-balanced robust routing algorithm for multi-granularity connection requests in traffic-grooming WDM mesh networks

The paper considers the problem of establishing robust routes for multi-granularity connection requests in traffic-grooming WDM mesh networks and proposes a novel Valiant load-balanced robust routing scheme for the hose uncertain model. Our objective is to minimize the total network cost when construct the stable virtual topology that assure robust routing for all possible multi-granularity connection requests under the hose model. Since the optimization problem is shown to be NP-complete, two heuristic algorithms are proposed and evaluated. Finally we compare the traffic throughput of the virtual topology by Valiant load-balanced robust routing scheme with that of the traditional traffic-grooming algorithm under the same total network cost by computer simulation.     

The Graph Neural Network Model

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IR^m that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.     

Designing a road network for hazardous materials shipments

We consider the problem of designating hazardous materials routes in and through a major population center. Initially, we restrict our attention to a minimally connected network (a tree) where we can predict accurately the flows on the network. We formulate the tree design problem as an integer programming problem with an objective of minimizing the total transport risk. Such design problems of moderate size can be solved using commercial solvers. We then develop a simple construction heuristic to expand the solution of the tree design problem by adding road segments. Such additions provide carriers with routing choices, which usually increase risks but reduce costs. The heuristic adds paths incrementally, which allows local authorities to trade off risk and cost. We use the road network of the city of Ravenna, Italy, to demonstrate the solution of our integer programming model and our path-addition heuristic.     

Two-stage clustering via neural networks

This paper presents a two-stage approach that is effective for performing fast clustering. First, a competitive neural network (CNN) that can harmonize mean squared error and information entropy criteria is employed to exploit the substructure in the input data by identifying the local density centers. A Gravitation neural network (GNN) then takes the locations of these centers as initial weight vectors and undergoes an unsupervised update process to group the centers into clusters. Each node (called gravi-node) in the GNN is associated with a finite attraction radius and would be attracted to a nearby centroid simultaneously during the update process, creating the Gravitation-like behavior without incurring complicated computations. This update process iterates until convergence and the converged centroid corresponds to a cluster. Compared to other clustering methods, the proposed clustering scheme is free of initialization problem and does not need to pre-specify the number of clusters. The two-stage approach is computationally efficient and has great flexibility in implementation. A fully parallel hardware implementation is very possible.     

短相遇接触时间网络环境中的时延容忍网络路由方案

为解决时延容忍网络在短接触时间下的路由问题,给出了一种短相遇接触时间网络环境中的时延容忍网络路由方案.该方案首先利用相遇接触时间、相遇间隔时间和消息时效等网络信息计算一跳传递概率和两跳传递概率,然后根据所得传递概率在当前接触节点和过去接触节点中选择转发节点,从而建立低成本路由.仿真性能分析结果表明:与其它经典的时延容忍网络路由方案相比,所给路由方案可以在降低路由成本的情况下,提高消息传递率,且能缩短平均时延.     

FPGA‐based adaptive dynamic sliding‐mode neural control for a brushless DC motor

In the adaptive neural control design, since the number of hidden neurons is finite for real‐time applications, the approximation errors introduced by the neural network cannot be inevitable. To ensure the stability of the adaptive neural control system, a switching compensator is designed to dispel the approximation error. However, it will lead to substantial chattering in the control effort. In this paper, an adaptive dynamic sliding‐mode neural control (ADSNC) system composed of a neural controller and a fuzzy compensator is proposed to tackle this problem. The neural controller, using a radial basis function neural network, is the main controller and the fuzzy compensator is designed to eliminate the approximation error introduced by the neural controller. Moreover, a proportional‐integral‐type adaptation learning algorithm is developed based on the Lyapunov function; thus not only the system stability can be guaranteed but also the convergence of the tracking error and controller parameters can speed up. Finally, the proposed ADSNC system is implemented based on a field programmable gate array chip for low‐cost and high‐performance industrial applications and is applied to control a brushless DC (BLDC) motor to show its effectiveness. The experimental results demonstrate the proposed ADSNC scheme can achieve favorable control performance without encountering chattering phenomena. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Segment Routing体系结构中的域内路由保护方案

学术界和工业界提出利用路由保护方案来提高域内路由协议应对故障的能力，从而加速网络故障恢复，降低由于网络故障引起的网络中断时间。目前互联网普遍采用的路由保护方案包括LFA和U-turn，由于它们的简单和高效，受到了互联网服务提供商的支持，但是这两种方案的单链路故障保护率较低。因此，段路由（Segment Routing，SR）被提出解决上述两种方案存在的问题，已有的针对SR的研究主要集中在其体系结构和应用场景。研究如何在SR中计算segments，将该问题表述为一个整数线性规划问题，提出一种两阶段的启发式算法（Two Phase Heuristic Algorithm，TPHA）求解该问题，将算法在不同网络拓扑中进行了模拟。模拟结果表明，TPHA的单链路故障保护率远远高于LFA和U-turn的单链路故障保护率。     

基于神经网络的动态路由选择算法

在分析了网络中基于QoS组播路由问题的基础上,文章给出了基于Hopfield神经网络的动态路由选择算法的模型。仿真研究表明该算法具有良好的分布特性和智能决策能力,此方案不仅保证了带宽、端到端延时和延时抖动,优化了路由树的代价,而且有效地控制了算法的复杂性,是一种快速动态组播路由算法,能实现全局网络资源利用的优化,容易扩展到大型网络中应用。     

基于灰色神经网络的烧结碱度预测与仿真

建立了应用灰色神经网络对烧机矿化学成分进行预测的有关理论，并在此基础上构造了灰色神经网络模型。该模型中。灰色理论弱化数据序列波动性和神经网络特有的非线性适应性信息处理能力相融合，本模型能在小样本贫信息的条件下对烧结矿碱度做出比较准确的预测。该模型具有预测精度高、所需样本少、计算简便等优点，取得了比较满意的结果。和BP神经网络算法相比，灰色神经网络算法有很大的应用前景和推广价值。