Journal articles: 'Network convergence'

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Bader, Joel S. "Grand network convergence." Genome Biology 12, no. 6 (2011): 306. http://dx.doi.org/10.1186/gb-2011-12-6-306.

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Bao, Chunhui, Yifei Pu, and Yi Zhang. "Fractional-Order Deep Backpropagation Neural Network." Computational Intelligence and Neuroscience 2018 (July 3, 2018): 1–10. http://dx.doi.org/10.1155/2018/7361628.

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In recent years, the research of artificial neural networks based on fractional calculus has attracted much attention. In this paper, we proposed a fractional-order deep backpropagation (BP) neural network model with L2 regularization. The proposed network was optimized by the fractional gradient descent method with Caputo derivative. We also illustrated the necessary conditions for the convergence of the proposed network. The influence of L2 regularization on the convergence was analyzed with the fractional-order variational method. The experiments have been performed on the MNIST dataset to demonstrate that the proposed network was deterministically convergent and can effectively avoid overfitting.

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Li, Jun Yi. "Bp Neural Network Optimized by PSO and its Application in Function Approximation." Advanced Materials Research 945-949 (June 2014): 2413–16. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2413.

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BP network is one of the most popular artificial neural networks because of its special advantage such as simple structure, distributed storage, parallel processing, high fault-tolerance performance, etc. However, with its extensive use in recent years, it is discovered that BP algorithm has the defects on slow convergent speed and easy convergence to a local minimum point. The paper proposes a method of BP Neural Network improved by Particle Swarm Optimization (PSO). The hybrid algorithm can not only avoid local minimum, but also raise the speed of network training and reduce the convergence time.

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Jamalipour, Abbas, Nei Kato, Hsiao-Hwa Chen, and Kyung Pyo Jun. "Broadband convergence Network (BcN)." Journal of Communications and Networks 8, no. 4 (December 2006): 363–68. http://dx.doi.org/10.1109/jcn.2006.6182784.

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Liu, Qingshan, Jinde Cao, and Guanrong Chen. "A Novel Recurrent Neural Network with Finite-Time Convergence for Linear Programming." Neural Computation 22, no. 11 (November 2010): 2962–78. http://dx.doi.org/10.1162/neco_a_00029.

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In this letter, a novel recurrent neural network based on the gradient method is proposed for solving linear programming problems. Finite-time convergence of the proposed neural network is proved by using the Lyapunov method. Compared with the existing neural networks for linear programming, the proposed neural network is globally convergent to exact optimal solutions in finite time, which is remarkable and rare in the literature of neural networks for optimization. Some numerical examples are given to show the effectiveness and excellent performance of the new recurrent neural network.

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Šíma, Jiří, Pekka Orponen, and Teemu Antti-Poika. "On the Computational Complexity of Binary and Analog Symmetric Hopfield Nets." Neural Computation 12, no. 12 (December 1, 2000): 2965–89. http://dx.doi.org/10.1162/089976600300014791.

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We investigate the computational properties of finite binary- and analog-state discrete-time symmetric Hopfield nets. For binary networks, we obtain a simulation of convergent asymmetric networks by symmetric networks with only a linear increase in network size and computation time. Then we analyze the convergence time of Hopfield nets in terms of the length of their bit representations. Here we construct an analog symmetric network whose convergence time exceeds the convergence time of any binary Hopfield net with the same representation length. Further, we prove that the MIN ENERGY problem for analog Hopfield nets is NP-hard and provide a polynomial time approximation algorithm for this problem in the case of binary nets. Finally, we show that symmetric analog nets with an external clock are computationally Turing universal.

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Whigham, P. A., G. Dick, and M. Parry. "Network rewiring dynamics with convergence towards a star network." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 472, no. 2194 (October 2016): 20160236. http://dx.doi.org/10.1098/rspa.2016.0236.

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Network rewiring as a method for producing a range of structures was first introduced in 1998 by Watts & Strogatz ( Nature 393 , 440–442. ( doi:10.1038/30918 )). This approach allowed a transition from regular through small-world to a random network. The subsequent interest in scale-free networks motivated a number of methods for developing rewiring approaches that converged to scale-free networks. This paper presents a rewiring algorithm (RtoS) for undirected, non-degenerate, fixed size networks that transitions from regular, through small-world and scale-free to star-like networks. Applications of the approach to models for the spread of infectious disease and fixation time for a simple genetics model are used to demonstrate the efficacy and application of the approach.

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Kobayakawa, Shunsuke, and Hirokazu Yokoi. "Predictor Using an Error-Convergence Neuron Network and its Application to Electrocardiograms." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 1 (January 20, 2011): 21–33. http://dx.doi.org/10.20965/jaciii.2011.p0021.

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The output error of a neuron network cannot converge at zero, even if the training for a neuron network is iterated many times. “Error-convergence neuron network” in which the output error of a singleoutput system uses neuron networks with multiplestep convergence, has been designed to resolve this problem. The output error is converged at zero by setting infinite steps of the neuron network. Three types of neuron network systems also have been designed. They are “Error-convergence parallel neuron network,” “Error-convergence recurrent neuron network,” and “Error-convergence parallel recurrent neuron network.” A subsequent prediction can be obtained by recurring the prediction of a predictor to its input if the predictor is free of prediction error. An error-convergence neuron network can be applied to realize this predictor. “Error-convergence neuron network predictor” has been proposed as such a predictor. In this study, its feasibility is investigated by performing prediction training for the errorconvergence neuron network predictor constructed of second-order Volterra neuron networks with two steps, using the nonlinear time series signal of a normal sinus rhythm electrocardiogram. Predictions without any error were obtained.

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Ahmed Ali, Adel, and Ahmed M. Al-Naamany. "Converged Networking: A Review of Concepts and Technologies." Sultan Qaboos University Journal for Science [SQUJS] 5 (December 1, 2000): 209. http://dx.doi.org/10.24200/squjs.vol5iss0pp209-225.

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Converged networking is an emerging technology thrust that integrates voice, video, and data traffic on a single network. Converged networking encompasses several aspects, all of which are related to the aggregation of networking activity. Such aspects include, Payload convergence, Protocol convergence, Physical convergence, Device convergence, Application convergence, Technology convergence, etc. In recent years the Internet has developed into a global data network that is highly accepted as a multimedia information platform, which has the potential to develop into an alternative carrier network in the future. Several convergence scenarios have been recently proposed, ranging from integrating communication services and computer application into two separate networks, to building a seamless multimedia network, which converges the Central Office based network and the Internet in a single network, thereby enabling telecommunications operators and service provider's tremendous investment in existing network infrastructure to be fully utilized. This paper offers introduction and review of the networking technologies. The paper presents the existing multiple networks into two infrastructures: an ATM/Frame Relay (Ethernet)- based corporate network with integrated voice, video, and data traffic and an Internet-based network for secure intranet, extranet and remote access. This work is aimed at summarizing the internetworking basics and technologies which are essential for the emerging converged networking systems. The specific areas addressed here are networking basics, networking technologies, types of traffic, and convergence of computer and communication networks.

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Cao, Xun. "Global Networks and Domestic Policy Convergence: A Network Explanation of Policy Changes." World Politics 64, no. 3 (June 27, 2012): 375–425. http://dx.doi.org/10.1017/s0043887112000081.

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National economies are embedded in complex networks such as trade, capital flows, and intergovernmental organizations (IGOs). These globalization forces impose differential impacts on national economies depending on a country's network positions. This article addresses the policy convergence-divergence debate by focusing on how networks at the international level affect domestic fiscal, monetary, and regulatory policies. The author presents two hypotheses: first, similarity in network positions induces convergence in domestic economic policies as a result of peer competitive pressure. Second, proximity in network positions facilitates policy learning and emulation, which result in policy convergence. The empirical analysis applies a latent-space model for relational/dyadic data and indicates that position similarity in the network of exports induces convergence in fiscal and regulatory policies; position similarity in the network of transnational portfolio investments induces convergence in fiscal policies; and position proximity in IGO networks is consistently associated with policy convergence in fiscal, monetary, and regulatory policies.

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Kamal Bashah, Nor Shahniza, Nor Haizon Husin, Syaripah Ruzaini Syed Aris, Norjansalika Janom, and Noor Habibah Arshad. "The optimization of leased line distribution at the EDGE of local access network via WAN convergence network." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (October 1, 2019): 333. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp333-341.

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Managing the multiple services of the leased line at the same location is quite critical especially when the resource become highly utilized. Bundling the several network resource components into a one box by simplifying the multiple networks to a single network can help to reduce the utilization of network resources. This paper presents a study on optimiziation of leased line distribution at the EDGE of Local Access Network via WAN convergence network. In this study, a WAN Convergence Network is designed which intends to use only a single leased line network in a location rather than multiple leased lines. It is using a simple concept of SDH structured which channelized the time slots and control the transmission line. The time slots will be relocated from the multiple leased lines into a WAN Convergence Network and it will be de-multiplexed through the Data Circuit Terminating Equipment (DCE) at the customer premises. The WAN Convergence Network design starts from the Digital Data Network (DDN) until the DCE which includes the Local Access Network. This approach will be able to save the network resource especially the time slots, cable port, DCE and consequently avoid adding new network infrastructure. This research will result to the new network design which offer multiple leased line networks at the customer end by using only one dedicated leased line network namely WAN Convergence Network.

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Schwab, Johannes, Stephan Antholzer, and Markus Haltmeier. "Big in Japan: Regularizing Networks for Solving Inverse Problems." Journal of Mathematical Imaging and Vision 62, no. 3 (October 3, 2019): 445–55. http://dx.doi.org/10.1007/s10851-019-00911-1.

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Abstract Deep learning and (deep) neural networks are emerging tools to address inverse problems and image reconstruction tasks. Despite outstanding performance, the mathematical analysis for solving inverse problems by neural networks is mostly missing. In this paper, we introduce and rigorously analyze families of deep regularizing neural networks (RegNets) of the form $$\mathbf {B}_\alpha + \mathbf {N}_{\theta (\alpha )} \mathbf {B}_\alpha $$Bα+Nθ(α)Bα, where $$\mathbf {B}_\alpha $$Bα is a classical regularization and the network $$\mathbf {N}_{\theta (\alpha )} \mathbf {B}_\alpha $$Nθ(α)Bα is trained to recover the missing part $${\text {Id}}_X - \mathbf {B}_\alpha $$IdX-Bα not found by the classical regularization. We show that these regularizing networks yield a convergent regularization method for solving inverse problems. Additionally, we derive convergence rates (quantitative error estimates) assuming a sufficient decay of the associated distance function. We demonstrate that our results recover existing convergence and convergence rates results for filter-based regularization methods as well as the recently introduced null space network as special cases. Numerical results are presented for a tomographic sparse data problem, which clearly demonstrate that the proposed RegNets improve classical regularization as well as the null space network.

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Sasikala, P. "Cloud Computing Towards Technological Convergence." International Journal of Cloud Applications and Computing 1, no. 4 (October 2011): 44–59. http://dx.doi.org/10.4018/ijcac.2011100104.

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With the popularization and improvement of social and industrial IT development, information appears to explosively increase, and people put much higher expectations on the services of computing, communication and network. Today’s public communication network is developing in the direction that networks are widely interconnected using communication network infrastructure as backbone and Internet protocols; at the same time, cloud computing, a computing paradigm in the ascendant, provides new service modes. Communication technology has the trend of developing towards computing technology and applications, and computing technology and applications have the trend of stepping towards service orientation architecture. Communication technology and information technology truly comes to a convergence. Telecom operators are planning to be providers of comprehensive information services in succession. To adopt cloud computing technology not only facilitates the upgrade of their communication network technology, service platform and supporting systems, but also facilitates the construction of the infrastructure and operating capacity of providing comprehensive information services. In this paper, the development processes of public communication network and computing are reviewed along with some new concepts for cloud computing.

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Tsioliaridou, Ageliki, and Vassilis Tsaoussidis. "Fast convergence to network fairness." Journal of Systems and Software 83, no. 5 (May 2010): 745–62. http://dx.doi.org/10.1016/j.jss.2009.11.715.

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Dixit, Sudhir. "On Fixed-Mobile Network Convergence." Wireless Personal Communications 38, no. 1 (May 17, 2006): 55–65. http://dx.doi.org/10.1007/s11277-006-9042-9.

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Rost, P., R. Boutaba, K. Doppler, and A. Gumaste. "Recent Advances in Network Convergence." Computer Networks 55, no. 7 (May 2011): 1455–58. http://dx.doi.org/10.1016/j.comnet.2011.04.002.

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Park, Sang-Joon, Jong-Chan Lee, and Sung-Yun Shin. "A Scheme of Access Network Management in Convergence Networks." Journal of the Korea Society of Computer and Information 17, no. 11 (November 30, 2012): 93–99. http://dx.doi.org/10.9708/jksci/2012.17.11.093.

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Cortés-Polo, David, José-Luis González-Sánchez, Francisco-Javier Rodríguez-Pérez, and Javier Carmona-Murillo. "Mobility management in packet transport networks for network convergence." Transactions on Emerging Telecommunications Technologies 26, no. 5 (September 24, 2013): 749–59. http://dx.doi.org/10.1002/ett.2705.

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Santos, Justino, Diogo Gomes, Susana Sargento, Rui L. Aguiar, Nigel Baker, Madiha Zafar, and Ahsan Ikram. "Multicast/broadcast network convergence in next generation mobile networks." Computer Networks 52, no. 1 (January 2008): 228–47. http://dx.doi.org/10.1016/j.comnet.2007.09.002.

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Chang, Gee-Kung, and Lin Cheng. "The benefits of convergence." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2062 (March 6, 2016): 20140442. http://dx.doi.org/10.1098/rsta.2014.0442.

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A multi-tier radio access network (RAN) combining the strength of fibre-optic and radio access technologies employing adaptive microwave photonics interfaces and radio-over-fibre (RoF) techniques is envisioned for future heterogeneous wireless communications. All-band radio spectrum from 0.1 to 100 GHz will be used to deliver wireless services with high capacity, high link speed and low latency. The multi-tier RAN will improve the cell-edge performance in an integrated heterogeneous environment enabled by fibre–wireless integration and networking for mobile fronthaul/backhaul, resource sharing and all-layer centralization of multiple standards with different frequency bands and modulation formats. In essence, this is a ‘no-more-cells’ architecture in which carrier aggregation among multiple frequency bands can be easily achieved with seamless handover between cells. In this way, current and future mobile network standards such as 4G and 5G can coexist with optimized and continuous cell coverage using multi-tier RoF regardless of the underlying network topology or protocol. In terms of users’ experience, the future-proof approach achieves the goals of system capacity, link speed, latency and continuous heterogeneous cell coverage while overcoming the bandwidth crunch in next-generation communication networks.

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Wódczak, Michal. "Convergence Aspects of Autonomic Cooperative Networks." International Journal of Information Technology and Web Engineering 6, no. 4 (October 2011): 51–62. http://dx.doi.org/10.4018/jitwe.2011100104.

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The current efforts across industry and academia are to develop new paradigms that enable ubiquitous on-demand service provision. This aim may be achievable because of the envisaged deployment of cutting-edge technologies such as cooperative transmission. However, a real advancement is only attainable when autonomic system design principles are taken into account. Looking at the concept of the Relay Enhanced Cell, one may come across commonalities with Mobile Ad-hoc Networks. Especially in Local Area scenarios, Base Stations seem to resemble advanced Access Points, while fixed and movable Relay Nodes might be replaced by powerful mobile User Terminals. On top of it, Generic Autonomic Network Architecture would help accommodate the fact that network devices may expose autonomic cooperative behaviors, allowing them to play certain roles. Finally, such a network must interact with Operations Support System deployed by the network operator for uninterrupted, continued operation.

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DA SILVA, IVAN NUNES, ANDRÉ NUNES DE SOUZA, and MÁRIO EDUARDO BORDON. "A NOVEL APPROACH FOR SOLVING CONSTRAINED NONLINEAR OPTIMIZATION PROBLEMS USING NEUROFUZZY SYSTEMS." International Journal of Neural Systems 11, no. 03 (June 2001): 281–86. http://dx.doi.org/10.1142/s0129065701000722.

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A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.

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Long, Yin, Xiao-Jun Zhang, and Kui Wang. "Convergence and approximate calculation of average degree under different network sizes for decreasing random birth-and-death networks." Modern Physics Letters B 32, no. 15 (May 24, 2018): 1850159. http://dx.doi.org/10.1142/s0217984918501592.

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In this paper, convergence and approximate calculation of average degree under different network sizes for decreasing random birth-and-death networks (RBDNs) are studied. First, we find and demonstrate that the average degree is convergent in the form of power law. Meanwhile, we discover that the ratios of the back items to front items of convergent reminder are independent of network link number for large network size, and we theoretically prove that the limit of the ratio is a constant. Moreover, since it is difficult to calculate the analytical solution of the average degree for large network sizes, we adopt numerical method to obtain approximate expression of the average degree to approximate its analytical solution. Finally, simulations are presented to verify our theoretical results.

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Kim, Junho, SeungGwan Lee, and Sungwon Lee. "Mesh Network Convergence Management System Using Software-Defined Network." Mobile Information Systems 2018 (October 17, 2018): 1–13. http://dx.doi.org/10.1155/2018/7157948.

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The Internet penetration rate is rising all over the world. In South Korea, there is no area which does not have Ethernet ports and Wi-Fi. So many people are familiar with network, Internet. However, there are still many countries and regions that do not know the weather today because the Internet is not available yet. To solve this problem, many people are studying the mesh network and actually building it and making efforts to spread the Internet. But, in reality, software that can build and manage such a mesh network is insufficient. In order to solve this problem, this paper proposes Gathering of Organization Treating Humble Ad-hoc Management (GOTHAM) and describes the results. GOTHAM is designed to solve three problems that exist in mesh network users. The first is that the mesh network is difficult to install, and the second is that there is no mesh network topology visualization software for batman-adv. And finally, there is no mesh network integration system for administrators. This paper focuses on these three problems and explains the GOTHAM that combines software-defined network (SDN). In addition, this paper describes three modules of GOTHAM. GOTHAM-setting, GOTHAM-main, and GOTHAM-GUI are explained in detail, and how these three modules work together is described. And also, we evaluate and analyze the performance of file transfer function using flow control which is a user application in the GOTHAM-main module. GOTHAM’s goal is not to be used for research but to actually run and let people use right now. That’s why GOTHAM is an open source project. All the software used in GOTHAM is open source. And also, we use hardware which is inexpensive and easy to get anywhere.

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Vantaggiato, Francesca P. "The drivers of regulatory networking: policy learning between homophily and convergence." Journal of Public Policy 39, no. 3 (June 19, 2018): 443–64. http://dx.doi.org/10.1017/s0143814x18000156.

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AbstractThe literature on transnational regulatory networks identified interdependence as their main rationale, downplaying domestic factors. Typically, relevant contributions use the word “network” only metaphorically. Yet, informal ties between regulators constitute networked structures of collaboration, which can be measured and explained. Regulators choose their frequent, regular network partners. What explains those choices? This article develops an Exponential Random Graph Model of the network of European national energy regulators to identify the drivers of informal regulatory networking. The results show that regulators tend to network with peers who regulate similarly organised market structures. Geography and European policy frameworks also play a role. Overall, the British regulator is significantly more active and influential than its peers, and a divide emerges between regulators from EU-15 and others. Therefore, formal frameworks of cooperation (i.e. a European Agency) were probably necessary to foster regulatory coordination across the EU.

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Kim, Yu-Doo, and Il-Young Moon. "P2P Network Simulation System for Performance Evaluation in Convergence Networks." Journal of information and communication convergence engineering 9, no. 4 (August 31, 2011): 396–400. http://dx.doi.org/10.6109/jicce.2011.9.4.396.

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Azeem, Shaikh, and Satyendra Sharma. "Convergence In Future Wireless Network Technology." Oriental journal of computer science and technology 10, no. 1 (February 8, 2017): 41–46. http://dx.doi.org/10.13005/ojcst/10.01.06.

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The communications sector is undergoing significant changes, with the emergence of a number of platforms available to provide a different range of services. Some of these platforms are complementary to each other, while others are competitive, or can provide a valid substitute for some of the services provided. Up till now, the most important communications platform in most of the developing countries has been the public switched telecommunication network (PSTN) which provides access to all households and buildings. This universality in providing access has also meant that the network has generally been designated as one for universal service.

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Naumer, Marcus J., Leonie Ratz, Yavor Yalachkov, Andrea Polony, Oliver Doehrmann, Vincent Van De Ven, Notger G. Müller, Jochen Kaiser, and Grit Hein. "Visuohaptic convergence in a corticocerebellar network." European Journal of Neuroscience 31, no. 10 (May 17, 2010): 1730–36. http://dx.doi.org/10.1111/j.1460-9568.2010.07208.x.

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Aggarwal, Vinay, Olaf Maennel, Jeffrey Mogul, and Allyn Romanow. "Workshop on network-I/O convergence." ACM SIGCOMM Computer Communication Review 33, no. 5 (October 2003): 75–80. http://dx.doi.org/10.1145/963985.963994.

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Zhang, Q. T., Jiayi Chen, and Hongbo Zhu. "Network convergence: theory, architectures, and applications." IEEE Wireless Communications 21, no. 6 (December 2014): 48–53. http://dx.doi.org/10.1109/mwc.2014.7000971.

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Cui, Hongyan, Shaohua Tang, Fangfang Sun, Yue Xu, and Xiaoli Yang. "Topological Embedding Feature Based Resource Allocation in Network Virtualization." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/271493.

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Virtualization provides a powerful way to run multiple virtual networks on a shared substrate network, which needs accurate and efficient mathematical models. Virtual network embedding is a challenge in network virtualization. In this paper, considering the degree of convergence when mapping a virtual network onto substrate network, we propose a new embedding algorithm based on topology mapping convergence-degree. Convergence-degree means the adjacent degree of virtual network’s nodes when they are mapped onto a substrate network. The contributions of our method are as below. Firstly, we map virtual nodes onto the substrate nodes with the maximum convergence-degree. The simulation results show that our proposed algorithm largely enhances the network utilization efficiency and decreases the complexity of the embedding problem. Secondly, we define the load balance rate to reflect the load balance of substrate links. The simulation results show our proposed algorithm achieves better load balance. Finally, based on the feature of star topology, we further improve our embedding algorithm and make it suitable for application in the star topology. The test result shows it gets better performance than previous works.

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Blaszczyk, Andreas, Reto Flückiger, Thomas Müller, and Carl-Olof Olsson. "Convergence behaviour of coupled pressure and thermal networks." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 33, no. 4 (July 1, 2014): 1233–50. http://dx.doi.org/10.1108/compel-12-2012-0378.

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Purpose – The purpose of this paper is to present a method for thermal computations of power devices based on a coupling between thermal and pressure networks. The concept of the coupling as well as the solution procedure is explained. The included examples demonstrate that the new method can be efficiently used for design of transformers and other power devices. Design/methodology/approach – The bidirectional propagation of temperature signal is introduced to the pressure network, which enables control of the power flow and a close coupling to the thermal network. The solution method is based on automatic splitting of the network definition (netlist) into two separate networks and iteratively solving the model using the Newton-Raphson approach as well as the adaptive relaxation enhanced by the direction change control. Findings – The proposed approach offers reliable convergence behaviour even for models with unknown direction of the fluid flow (bidirectional flows). The accuracy is sufficient for engineering applications and comparable with the computational fluid dynamics method. The computation times in the range of milliseconds and seconds are attractive for using the method in engineering design tools. Originality/value – The new method can be considered as a foundation for a consistent network modelling system of arbitrary thermodynamic problems including fluid flow. Such a modelling system can be used directly by device designers since the complexity of thermodynamic formulations is encapsulated in predefined network elements while the numerical solution is based on a standard network description and solvers (Spice).

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YEUNG, DANIEL S., SHENSHAN QIU, ERIC C. C. TSANG, and XIZHAO WANG. "A GENERAL UPDATING RULE FOR DISCRETE HOPFIELD-TYPE NEURAL NETWORK WITH TIME-DELAY AND THE CORRESPONDING SEARCH ALGORITHM." International Journal of Computational Intelligence and Applications 01, no. 04 (December 2001): 399–412. http://dx.doi.org/10.1142/s1469026801000329.

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In this paper, the Hopfield neural network with delay (HNND) is studied from the standpoint of regarding it as an optimizing computational model. Two general updating rules for networks with delay (GURD) are given based on Hopfield-type neural networks with delay for optimization problems and characterized by dynamic thresholds. It is proved that in any sequence of updating rule modes, the GURD monotonously converges to a stable state of the network. The diagonal elements of the connection matrix are shown to have an important influence on the convergence process, and they represent the relationship of the local maximum value of the energy function to the stable states of the networks. All the ordinary discrete Hopfield neural network (DHNN) algorithms are instances of the GURD. It can be shown that the convergence conditions of the GURD may be relaxed in the context of applications, for instance, the condition of nonnegative diagonal elements of the connection matrix can be removed from the original convergence theorem. A new updating rule mode and restrictive conditions can guarantee the network to achieve a local maximum of the energy function with a step-by-step algorithm. The convergence rate improves evidently when compared with other methods. For a delay item considered as a noise disturbance item, the step-by-step algorithm demonstrates its efficiency and a high convergence rate. Experimental results support our proposed algorithm.

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Cheng, Zhou, and Tao Juncheng. "Adaptive combination forecasting model for China’s logistics freight volume based on an improved PSO-BP neural network." Kybernetes 44, no. 4 (April 7, 2015): 646–66. http://dx.doi.org/10.1108/k-09-2014-0201.

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Purpose – To accurately forecast logistics freight volume plays a vital part in rational planning formulation for a country. The purpose of this paper is to contribute to developing a novel combination forecasting model to predict China’s logistics freight volume, in which an improved PSO-BP neural network is proposed to determine the combination weights. Design/methodology/approach – Since BP neural network has the ability of learning, storing, and recalling information that given by individual forecasting models, it is effective in determining the combination weights of combination forecasting model. First, an improved PSO based on simulated annealing method and space-time adjustment strategy (SAPSO) is proposed to solve out the connection weights of BP neural network, which overcomes the problems of local optimum traps, low precision and poor convergence during BP neural network training process. Then, a novel combination forecast model based on SAPSO-BP neural network is established. Findings – Simulation tests prove that the proposed SAPSO has better convergence performance and more stability. At the same time, combination forecasting models based on three types of BP neural networks are developed, which rank as SAPSO-BP, PSO-BP and BP in accordance with mean absolute percentage error (MAPE) and convergent speed. Also the proposed combination model based on SAPSO-BP shows its superiority, compared with some other combination weight assignment methods. Originality/value – SAPSO-BP neural network is an original contribution to the combination weight assignment methods of combination forecasting model, which has better convergence performance and more stability.

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Chen, Y., G. Gao, S. B. Liao, H. Y. Yang, and S. Wang. "The Convergence Scheme on Network Utility Maximization in Wireless Multicast Networks." Journal of Applied Research and Technology 11, no. 4 (August 2013): 533–39. http://dx.doi.org/10.1016/s1665-6423(13)71560-8.

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Devikar, Rohit Nilkanth, D. V. Patil, and V. Chandraprakash. "Study of BGP Convergence Time." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (February 1, 2016): 413. http://dx.doi.org/10.11591/ijece.v6i1.8106.

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Border Gateway Protocol (BGP), a path vector routing protocol, is a widespread exterior gateway protocol (EGP) in the internet. Extensive deployment of the new technologies in internet, protocols need to have continuous improvements in its behavior and operations. New routing technologies conserve a top level of service availability. Hence, due to topological changes, BGP needs to achieve a fast network convergence. Now a days size of the network growing very rapidly. To maintain the high scalability in the network BGP needs to avoid instability. The instability and failures may cause the network into an unstable state, which significantly increases the network convergence time. This paper summarizes the various approaches like BGP policies, instability, and fault detection etc. to improve the convergence time of BGP.

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Devikar, Rohit Nilkanth, D. V. Patil, and V. Chandraprakash. "Study of BGP Convergence Time." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (February 1, 2016): 413. http://dx.doi.org/10.11591/ijece.v6i1.pp413-420.

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Border Gateway Protocol (BGP), a path vector routing protocol, is a widespread exterior gateway protocol (EGP) in the internet. Extensive deployment of the new technologies in internet, protocols need to have continuous improvements in its behavior and operations. New routing technologies conserve a top level of service availability. Hence, due to topological changes, BGP needs to achieve a fast network convergence. Now a days size of the network growing very rapidly. To maintain the high scalability in the network BGP needs to avoid instability. The instability and failures may cause the network into an unstable state, which significantly increases the network convergence time. This paper summarizes the various approaches like BGP policies, instability, and fault detection etc. to improve the convergence time of BGP.

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Chen, Yi, Ge Gao, and Sai Wang. "Study on Rate Convergence in Distributed Wireless Mesh Network." Applied Mechanics and Materials 220-223 (November 2012): 1813–16. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1813.

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One of the major factors that affects the distributed network utility maximization(NUM) is the exchange message delay which leads to oscillations due to imperfect response to time-variant channel. In this paper, a novel Fuzzy technique is presented to solve the reverse impact control delay in distributed NUM of wireless mesh networks. Simulation results illustrate better performance.

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Bolanowski, Marek, and Tomasz Byczek. "Measure and compare the convergence time of network routing protocols." ITM Web of Conferences 21 (2018): 00013. http://dx.doi.org/10.1051/itmconf/20182100013.

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Currently, IT systems require more and more reliability. It can be guaranteed only by using redundancy both in the connectivity and network devices. The authors attempted to measure the convergence time for EIGRP and OSPF routing protocols after link or network nodes failure. Research have been conducted with the real devices and hardware traffic generator. In case of both protocols, failures have been simulated in various places in the network topology. On this basis, time in which tested protocol restored full connectivity for a specific topology with backup links have been precisely determined. Knowledge of the time required to restore the network after a failure can be useful during designing services based on networks with implemented routing. This can improve a tolerance of connectivity interruptions.

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PARK, YOUNG-KEUN, and VLADIMIR CHERKASSKY. "NEURAL NETWORK FOR CONTROL OF REARRANGEABLE CLOS NETWORKS." International Journal of Neural Systems 05, no. 03 (September 1994): 195–205. http://dx.doi.org/10.1142/s0129065794000219.

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Rapid evolution in the field of communication networks requires high speed switching technologies. This involves a high degree of parallelism in switching control and routing performed at the hardware level. The multistage crossbar networks have always been attractive to switch designers. In this paper a neural network approach to controlling a three-stage Clos network in real time is proposed. This controller provides optimal routing of communication traffic requests on a call-by-call basis by rearranging existing connections, with a minimum length of rearrangement sequence so that a new blocked call request can be accommodated. The proposed neural network controller uses Paull’s rearrangement algorithm, along with the special (least used) switch selection rule in order to minimize the length of rearrangement sequences. The functional behavior of our model is verified by simulations and it is shown that the convergence time required for finding an optimal solution is constant, regardless of the switching network size. The performance is evaluated for random traffic with various traffic loads. Simulation results show that applying the least used switch selection rule increases the efficiency in switch rearrangements, reducing the network convergence time. The implementation aspects are also discussed to show the feasibility of the proposed approach.

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Wolf, Frederik, Aiko Voigt, and Reik V. Donner. "A climate network perspective on the intertropical convergence zone." Earth System Dynamics 12, no. 1 (March 31, 2021): 353–66. http://dx.doi.org/10.5194/esd-12-353-2021.

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Abstract. The intertropical convergence zone (ITCZ) is an important component of the tropical rain belt. Climate models continue to struggle to adequately represent the ITCZ and differ substantially in its simulated response to climate change. Here we employ complex network approaches, which extract spatiotemporal variability patterns from climate data, to better understand differences in the dynamics of the ITCZ in state-of-the-art global circulation models (GCMs). For this purpose, we study simulations with 14 GCMs in an idealized slab-ocean aquaplanet setup from TRACMIP – the Tropical Rain belts with an Annual cycle and a Continent Model Intercomparison Project. We construct network representations based on the spatial correlation patterns of monthly surface temperature anomalies and study the zonal-mean patterns of different topological and spatial network characteristics. Specifically, we cluster the GCMs by means of the distributions of their zonal network measures utilizing hierarchical clustering. We find that in the control simulation, the distributions of the zonal network measures are able to pick up model differences in the tropical sea surface temperature (SST) contrast, the ITCZ position, and the strength of the Southern Hemisphere Hadley cell. Although we do not find evidence for consistent modifications in the network structure tracing the response of the ITCZ to global warming in the considered model ensemble, our analysis demonstrates that coherent variations of the global SST field are linked to ITCZ dynamics. This suggests that climate networks can provide a new perspective on ITCZ dynamics and model differences therein.

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Jónsson, Hlynur, Giovanni Cherubini, and Evangelos Eleftheriou. "Convergence Behavior of DNNs with Mutual-Information-Based Regularization." Entropy 22, no. 7 (June 30, 2020): 727. http://dx.doi.org/10.3390/e22070727.

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Information theory concepts are leveraged with the goal of better understanding and improving Deep Neural Networks (DNNs). The information plane of neural networks describes the behavior during training of the mutual information at various depths between input/output and hidden-layer variables. Previous analysis revealed that most of the training epochs are spent on compressing the input, in some networks where finiteness of the mutual information can be established. However, the estimation of mutual information is nontrivial for high-dimensional continuous random variables. Therefore, the computation of the mutual information for DNNs and its visualization on the information plane mostly focused on low-complexity fully connected networks. In fact, even the existence of the compression phase in complex DNNs has been questioned and viewed as an open problem. In this paper, we present the convergence of mutual information on the information plane for a high-dimensional VGG-16 Convolutional Neural Network (CNN) by resorting to Mutual Information Neural Estimation (MINE), thus confirming and extending the results obtained with low-dimensional fully connected networks. Furthermore, we demonstrate the benefits of regularizing a network, especially for a large number of training epochs, by adopting mutual information estimates as additional terms in the loss function characteristic of the network. Experimental results show that the regularization stabilizes the test accuracy and significantly reduces its variance.

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Lücke, Jörg, Christian Keck, and Christoph von der Malsburg. "Rapid Convergence to Feature Layer Correspondences." Neural Computation 20, no. 10 (October 2008): 2441–63. http://dx.doi.org/10.1162/neco.2008.06-07-539.

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We describe a neural network able to rapidly establish correspondence between neural feature layers. Each of the network's two layers consists of interconnected cortical columns, and each column consists of inhibitorily coupled subpopulations of excitatory neurons. The dynamics of the system builds on a dynamic model of a single column, which is consistent with recent experimental findings. The network realizes dynamic links between its layers with the help of specialized columns that evaluate similarities between the activity distributions of local feature cell populations, are subject to a topology constraint, and can gate the transfer of feature information between the neural layers. The system can robustly be applied to natural images, and correspondences are found in time intervals estimated to be smaller than 100 ms in physiological terms.

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Hazazi, Muhammad Asaduddin, and Agus Sihabuddin. "Extended Kalman Filter In Recurrent Neural Network: USDIDR Forecasting Case Study." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 13, no. 3 (July 31, 2019): 293. http://dx.doi.org/10.22146/ijccs.47802.

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Artificial Neural Networks (ANN) especially Recurrent Neural Network (RNN) have been widely used to predict currency exchange rates. The learning algorithm that is commonly used in ANN is Stochastic Gradient Descent (SGD). One of the advantages of SGD is that the computational time needed is relatively short. But SGD also has weaknesses, including SGD requiring several hyperparameters such as the regularization parameter. Besides that SGD relatively requires a lot of epoch to reach convergence. Extended Kalman Filter (EKF) as a learning algorithm on RNN is used to replace SGD with the hope of a better level of accuracy and convergence rate. This study uses IDR / USD exchange rate data from 31 August 2015 to 29 August 2018 with 70% data as training data and 30% data as test data. This research shows that RNN-EKF produces better convergent speeds and better accuracy compared to RNN-SGD.

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Bai, Bo, Zhigang Cao, Wei Chen, and I. Chih-Lin. "Wireless communication and broadcasting convergence network throughput." Tsinghua Science and Technology 14, no. 6 (December 2009): 710–17. http://dx.doi.org/10.1016/s1007-0214(09)70139-7.

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Mišić, Bratislav, Joaquín Goñi, Richard F. Betzel, Olaf Sporns, and Anthony R. McIntosh. "A Network Convergence Zone in the Hippocampus." PLoS Computational Biology 10, no. 12 (December 4, 2014): e1003982. http://dx.doi.org/10.1371/journal.pcbi.1003982.

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Chatterjee, Samir, Amitava Dutta, and Vinay B. Chandhok. "Introduction—Network Convergence: Issues, Trends and Future." Information Systems Frontiers 6, no. 3 (September 2004): 183–88. http://dx.doi.org/10.1023/b:isfi.0000037888.83266.3b.

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Lu, Wenlian, Libin Rong, and Tianping Chen. "Global Convergence of Delayed Neural Network Systems." International Journal of Neural Systems 13, no. 03 (June 2003): 193–204. http://dx.doi.org/10.1142/s0129065703001534.

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In this paper, without assuming the boundedness, strict monotonicity and differentiability of the activation functions, we utilize a new Lyapunov function to analyze the global convergence of a class of neural networks models with time delays. A new sufficient condition guaranteeing the existence, uniqueness and global exponential stability of the equilibrium point is derived. This stability criterion imposes constraints on the feedback matrices independently of the delay parameters. The result is compared with some previous works. Furthermore, the condition may be less restrictive in the case that the activation functions are hyperbolic tangent.

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Chung, Chung Joo, and Han Woo Park. "Beyond data, innovation, social network, and convergence." Quality & Quantity 52, no. 2 (December 12, 2017): 515–18. http://dx.doi.org/10.1007/s11135-017-0669-2.

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Sapoetra, Yudistira Arya, Azwar Riza Habibi, and Lukman Hakim. "Random Number Generator Untuk Bobot Metode Conjugate Gradient Neural Network." Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika 4, no. 1 (April 12, 2019): 19–25. http://dx.doi.org/10.31316/j.derivat.v4i1.161.

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This research develops the theory of NN (neural network) by using CG (conjugate gradient) to speed up the process of convergence on a network of NN. CG algorithm is an iterative algorithm to solve simultaneous linear equations on a large scale and it is used to optimize the process of the network on backpropagation. In the process, a Neural netwok doing random weighting on the weight of v and w and this weight will have an effect on the speed of convergence of an algorithm for NN by the method of CG. Furthermore, generating the random numbers to take a sample as a generator in this research of neural network by using uniform distribution (0,1) methods. Therefore, the aims of this research are to improve the convergence on NN weighting using numbers which are generated randomly by the generator and the will be corrected with the CG method.Keywords: neural network, backpropagation, weighting, conjugate gradient

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Journal articles on the topic 'Network convergence'

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