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Journal articles on the topic 'Discrete network design'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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1

Chen, Qun, and Feng Shi. "Model for Microcirculation Transportation Network Design." Mathematical Problems in Engineering 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/379867.

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The idea of microcirculation transportation was proposed to shunt heavy traffic on arterial roads through branch roads. The optimization model for designing micro-circulation transportation network was developed to pick out branch roads as traffic-shunting channels and determine their required capacity, trying to minimize the total reconstruction expense and land occupancy subject to saturation and reconstruction space constraints, while accounting for the route choice behaviour of network users. Since micro-circulation transportation network design problem includes both discrete and continuous variables, a discretization method was developed to convert two groups of variables (discrete variables and continuous variables) into one group of new discrete variables, transforming the mixed network design problem into a new kind of discrete network design problem with multiple values. The genetic algorithm was proposed to solve the new discrete network design problem. Finally a numerical example demonstrated the efficiency of the model and algorithm.

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Wang, Yang, Jin Xin Cao, Xia Xi Li, and Ri Dong Wang. "Time-Dependent Discrete Transportation Network Design." Applied Mechanics and Materials 505-506 (January 2014): 533–36. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.533.

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The transportation network construction takes place over a quite long time span and need enough budget. The budget is from the allocation of funds in phases and the construction cost change in the process of the construction. The general static transportation network design problems ignores the problems above. So the optimal solution obtained by the static model is best in short time, and it is may be unfeasible in the actual situation. Based on the actual situation and the shortage of the static model, the time-dependent transport network design is proposed in this study. The plan horizon is divided into N intervals and a bi-level model is built to describe the problem. The objective of the upper-level is to minimize the total cost of the whole stages. the lower-level model is a user equilibrium model. Then the branch and bound (B-B) algorithm is designed to solve the model. It is obvious that the solution of the time-dependent simulation model is more feasible than the solution of the static sequential design.

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3

Xie, Chi. "Bicriterion discrete equilibrium network design problem." Networks 63, no. 4 (2014): 286–305. http://dx.doi.org/10.1002/net.21546.

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Juan, Wu, Lu Huapu, Yu Xinxin, and Bian Changzhi. "Genetic Algorithm for Multiuser Discrete Network Design Problem under Demand Uncertainty." Mathematical Problems in Engineering 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/686272.

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Discrete network design is an important part of urban transportation planning. The purpose of this paper is to present a bilevel model for discrete network design. The upper-level model aims to minimize the total travel time under a stochastic demand to design a discrete network. In the lower-level model, demands are assigned to the network through a multiuser traffic equilibrium assignment. Generally, discrete network could affect path selections of demands, while the results of the multiuser traffic equilibrium assignment need to reconstruct a new discrete network. An iterative approach including an improved genetic algorithm and Frank-Wolfe algorithm is used to solve the bi-level model. The numerical results on Nguyen Dupuis network show that the model and the related algorithms were effective for discrete network design.

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O’brien, Liam, and Szeto Wai Yuen. "The Discrete Network Design Problem Over Time." HKIE Transactions 14, no. 4 (2007): 47–55. http://dx.doi.org/10.1080/1023697x.2007.10668097.

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Belotti, P., F. Malucelli, and L. Brunetta. "Multicommodity network design with discrete node costs." Networks 49, no. 1 (2006): 90–99. http://dx.doi.org/10.1002/net.20144.

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Miandoabchi, Elnaz, Farzaneh Daneshzand, W. Y. Szeto, and Reza Zanjirani Farahani. "Multi-objective discrete urban road network design." Computers & Operations Research 40, no. 10 (2013): 2429–49. http://dx.doi.org/10.1016/j.cor.2013.03.016.

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Zhou, Yaling, Chengxuan Cao, and Ziyan Feng. "Optimization of Multimodal Discrete Network Design Problems Based on Super Networks." Applied Sciences 11, no. 21 (2021): 10143. http://dx.doi.org/10.3390/app112110143.

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In this paper, we investigate the multimodal discrete network design problem that simultaneously optimizes the car, bus, and rail transit network, in which inter-modal transfers are achieved by slow traffic modes including walking and bike-sharing. Specifically, a super network topology is presented to signify the modal interactions. Then, the generalized cost formulas of each type of links in the super network are defined. And based on the above formulas a bi-objective programming model is proposed to minimize the network operation cost and construction cost with traffic flow equilibrium constraints, investment constraints and expansion constraints. Moreover, a hybrid heuristic algorithm that combines the minimum cost flow algorithm and simulated annealing algorithm is presented to solve the proposed model. Finally, the effectiveness of the proposed model and algorithm is evaluated through two numerical tests: a simple test network and an actual multimodal transport network.

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Yin, Ruyang, Jiping Xing, Pengli Mo, Nan Zheng, and Zhiyuan Liu. "BO-B&B: A hybrid algorithm based on Bayesian optimization and branch-and-bound for discrete network design problems." Electronic Research Archive 30, no. 11 (2022): 3993–4014. http://dx.doi.org/10.3934/era.2022203.

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<abstract> <p>A discrete network design problem (DNDP) is conventionally formulated as an analytical bi-level programming problem to acquire an optimal network design strategy for an existing traffic network. In recent years, multimodal network design problems have benefited from simulation-based models. The nonconvexity and implicity of bi-level DNDPs make it challenging to obtain an optimal solution, especially for simulation-related models. Bayesian optimization (BO) has been proven to be an effective method for optimizing the costly black-box functions of simulation-based continuous network design problems. However, there are only discrete inputs in DNDPs, which cannot be processed using standard BO algorithms. To address this issue, we develop a hybrid method (BO-B&amp;B) that combines Bayesian optimization and a branch-and-bound algorithm to deal with discrete variables. The proposed algorithm exploits the advantages of the cutting-edge machine-learning parameter-tuning technique and the exact mathematical optimization method, thereby balancing efficiency and accuracy. Our experimental results show that the proposed method outperforms benchmarking discrete optimization heuristics for simulation-based DNDPs in terms of total computational time. Thus, BO-B&amp;B can potentially aid decision makers in mapping practical network design schemes for large-scale networks.</p> </abstract>

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10

Hu, Guojing, Weike Lu, Feng Wang, and Robert W. Whalin. "Macroscopic Fundamental Diagram Based Discrete Transportation Network Design." Journal of Advanced Transportation 2020 (January 20, 2020): 1–13. http://dx.doi.org/10.1155/2020/4951953.

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The presence of demand uncertainty brings challenges to network design problems (NDP), because fluctuations in origin-destination (OD) demand have a prominent effect on the corresponding total travel time, which is usually adopted as an index to evaluate the network design problem. Fortunately, the macroscopic fundamental diagram (MFD) has been proved to be a property of the road network itself, independent of the origin-destination demand. Such characteristics of an MFD provide a new theoretical basis to assess the traffic network performance and further appraise the quality of network design strategies. Focusing on improving network capacity under the NDP framework, this paper formulates a bi-level programming model, where at the lower level, flows are assigned to the newly extended network subject to user equilibrium theory, and the upper level determines which links should be added to achieve the maximum network capacity. To solve the proposed model, we design an algorithm framework, where traffic flow distribution of each building strategy is calculated under the dynamic user equilibrium (DUE), and updated through the VISSIM-COM-Python interaction. Then, the output data are obtained to shape MFDs, and k-means clustering algorithm is employed to quantify the MFD-based network capacity. Finally, the methodology is implemented in a test network, and the results show the benefits of using the MFD-based method to solve the network design problem under stochastic OD demands. Specifically, the capacity paradox is also presented in the test results.

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Zhang, Lihui, Hai Yang, Di Wu, and Dianhai Wang. "Solving a discrete multimodal transportation network design problem." Transportation Research Part C: Emerging Technologies 49 (December 2014): 73–86. http://dx.doi.org/10.1016/j.trc.2014.10.008.

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Wang, Guangmin, Ziyou Gao, and Meng Xu. "Integrating link-based discrete credit charging scheme into discrete network design problem." European Journal of Operational Research 272, no. 1 (2019): 176–87. http://dx.doi.org/10.1016/j.ejor.2018.05.069.

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Jagannathan, S. "Admission controller design for high-speed networks: A hybrid system approach." Journal of High Speed Networks 14, no. 3 (2005): 263–81. https://doi.org/10.3233/hsn-2005-266.

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A novel real-time discrete-event admission control (AC) scheme for high-speed networks is proposed with the aim of attaining a desired quality of service (QoS) and high network utilization. The AC uses the available capacity from a novel adaptive bandwidth estimation scheme, a congestion indicator derived from a congestion controller, Peak bit/cell Rate (PBR/PCR) estimate from new sources, along with the desired QoS metrics, and makes decisions whether to “admit” or “reject” new sources so that the QoS metrics and network utilization are met. The admission controller dynamics are expressed as a discrete-event system while the bandwidth estimator design-based on Lyapunov theory and the congestion controller dynamics are given in discrete-time. The novel aspect of the proposed approach is the application of hybrid system theory to prove the performance of the proposed admission controller, stability and the development of rigorous and repeatable design procedure. The performance of the proposed AC is evaluated using the QoS metrics, which are given in terms of service delay, packet/cell losses, and network utilization. Simulation results are presented by streaming ON/OFF and MPEG video data into the network. Results show that the proposed AC admits significantly more traffic compared to other available admission control schemes thereby guaranteeing high network utilization while maintaining the desired QoS.

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Babajans, Ruslans, Darja Cirjulina, Filips Capligins, Deniss Kolosovs, and Anna Litvinenko. "Synchronization of Analog-Discrete Chaotic Systems for Wireless Sensor Network Design." Applied Sciences 14, no. 2 (2024): 915. http://dx.doi.org/10.3390/app14020915.

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The current work is focused on studying the performance of the Pecora–Carroll synchronization technique to achieve synchronization between the analog and discrete chaos oscillators. The importance of this study is supported by the growing applications of chaotic systems for improving the security of data transmission in various communication layers, primarily on the physical layer. The hybrid analog-discrete approach of implementing chaos oscillators opens new possible communication schemes for wireless sensor network (WSN) applications. The analog implementation of chaos oscillators can benefit the simpler sensor node (SN) integration, while the discrete implementation can be used on the gateway. However, the core of such chaos-based communications is synchronizing analog and discrete chaos oscillators. This work studies two key parameters of analog-discrete chaotic synchronization: chaotic synchronization noise immunity and synchronization speed. The noise immunity study demonstrates the quality of synchronization at various noise levels, while the synchronization speed demonstrates how quickly the analog-discrete synchronization is achieved, along with how quickly the two systems diverge when synchronization is no longer present. The two studies use both simulation-based and hardware-based approaches. In the simulation case, the analog oscillator’s circuit is modeled in LTspice XVII, while in the hardware case, the circuit is implemented on the PCB. In both simulation and hardware studies, the discrete model of the oscillator is implemented in MATLAB R2023b. The studies are performed for two pairs of different chaos oscillators to widen the proposed approach application potential: the Vilnius and RC chaos oscillators. The oscillators have been selected due to their simplicity and similar dynamic behavior for model-based and electrical circuit implementation. The proposed approach also allows us to compare the synchronization of different oscillators in the analog-discrete implementation.

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15

Fontaine, Pirmin, and Stefan Minner. "A dynamic discrete network design problem for maintenance planning in traffic networks." Annals of Operations Research 253, no. 2 (2016): 757–72. http://dx.doi.org/10.1007/s10479-016-2171-y.

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16

Khan, Gohar, Manar Mohaisen, and Matthias Trier. "The network ROI." Internet Research 30, no. 2 (2019): 631–52. http://dx.doi.org/10.1108/intr-07-2018-0346.

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Purpose Leveraging social action theory, social network theory and the notion of network externality, the purpose of this paper is to model two different return on investment (ROI) measures: the networked ROI which captures the network effect originating from a social media investment, and the discrete ROI which focuses social media discrete returns from individual users. Design/methodology/approach A field experiment was set up over a period of three months to test the effects of two variants of an advertisement campaign (a social vs a discrete ad) on the modeled networked and discrete ROIs. Findings The authors find that emphasizing discrete user actions leads to lower network gains, but higher monetary returns while the social action emphasis produces higher network gains, but lower monetary returns. The study further suggests that social action focus is preferable for brand promotion and engagement, whereas the discrete action focus is suitable for boosting sales and website traffic. Practical implications Several potential implications for social media researchers and marketers are also discussed. Originality/value The authors for the first time showed that that the social media returns are derived not only from individual actions taken by the user (e.g. likes and shares) but also from users’ social interdependencies and the additional exposure that results from network effects.

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Wang, Yang, Jin Xin Cao, Ri Dong Wang, and Xia Xi Li. "Research on Uncertain Network Design Problem." Applied Mechanics and Materials 505-506 (January 2014): 613–18. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.613.

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In this study, a kind of uncertain network design problem, network design problem under uncertain construction cost, is researched.The discrete network design problem under uncertain construction costs deals with the selection of links to be added to the existing network, so as to minimize the total travel costs in the network. It is assumed that the value of the demand between each pair of origin and destination is a constant and the construction costs of each potential link addition follow a certain stochastic distribution. In this paper, a bi-level and stochastic programming model for the discrete network design problem is proposed. The construction costs of potential links are assumed as random variables and mutually independent with each other in this model. The upper-level model is a chance constrain model with the objective function of minimizing the total travel costs in the network, and the lower-level model is a user equilibrium model. The stochastic model is then transformed into a deterministic one. A branch-and-bound solution algorithm is designed to solve the deterministic model in an efficient way. At last, a computational experiment is conducted to illustrate the effectiveness and efficiency of the approach proposed in this paper. The results show that the stochastic model is more flexible and practical compared with the deterministic one.

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18

Kim, Byung Jong, Wonkyu Kim, and Byung Heum Song. "Sequencing and scheduling highway network expansion using a discrete network design model." Annals of Regional Science 42, no. 3 (2007): 621–42. http://dx.doi.org/10.1007/s00168-007-0170-2.

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19

Ren, Jin Sheng, Guang Chun Luo, and Ke Qin. "Design Chaotic Neural Network from Discrete Time Feedback Function." Applied Mechanics and Materials 339 (July 2013): 366–71. http://dx.doi.org/10.4028/www.scientific.net/amm.339.366.

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The goal of this paper is to give a universal design methodology of a Chaotic Neural Net-work (CNN). By appropriately choosing self-feedback, coupling functions and external stimulus, we have succeeded in proving a dynamical system defined by discrete time feedback equations possess-ing interesting chaotic properties. The sufficient conditions of chaos are analyzed by using Jacobian matrix, diagonal dominant matrix and Lyapunov Exponent (LE). Experiments are also conducted un-der a simple data set. The results confirm the theorem's correctness. As far as we know, both the experimental and theoretical results presented here are novel.

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20

Szeto, W. Y., Xiaoqing Jaber, and Margaret O’Mahony. "Time-Dependent Discrete Network Design Frameworks Considering Land Use." Computer-Aided Civil and Infrastructure Engineering 25, no. 6 (2010): 411–26. http://dx.doi.org/10.1111/j.1467-8667.2010.00654.x.

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21

Haas, Inbal, and Shlomo Bekhor. "A parsimonious heuristic for the discrete network design problem." Transportmetrica A: Transport Science 12, no. 1 (2015): 43–64. http://dx.doi.org/10.1080/23249935.2015.1094680.

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Wang, Zhongqi, Qi Han, and Bauke de Vries. "Land use oriented bi-level discrete road network design." Transportation Research Procedia 37 (2019): 35–42. http://dx.doi.org/10.1016/j.trpro.2018.12.163.

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Wang, Shuaian, Qiang Meng, and Hai Yang. "Global optimization methods for the discrete network design problem." Transportation Research Part B: Methodological 50 (April 2013): 42–60. http://dx.doi.org/10.1016/j.trb.2013.01.006.

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24

Oriolo, Gianpaolo, Laura Sanità, and Rico Zenklusen. "Network design with a discrete set of traffic matrices." Operations Research Letters 41, no. 4 (2013): 390–96. http://dx.doi.org/10.1016/j.orl.2013.04.011.

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Huang, Yanyan, Hongli Zhao, Yunzhong Jiang, Xin Lu, Zheng Hao, and Hao Duan. "Comparison and Analysis of Different Discrete Methods and Entropy-Based Methods in Rain Gauge Network Design." Water 11, no. 7 (2019): 1357. http://dx.doi.org/10.3390/w11071357.

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A reasonable rain gauge network layout can provide accurate regional rainfall data and effectively support the monitoring, development and utilization of water resources. Currently, an increasing number of network design methods based on entropy targets are being applied to network design. The discretization of data is a common method of obtaining the probability in calculations of information entropy. To study the application of different discretization methods and different entropy-based methods in the design of rain gauge networks, this paper compares and analyzes 9 design results for rainy season rain gauge networks using three commonly used discretization methods (A1, SC and ST) and three entropy-based network design algorithms (MIMR, HT and HC) from three perspectives: the joint entropy, spatiality, and accuracy of the network, as evaluation indices. The results show that the variation in network information calculated by the A1 and ST methods for rainy season rain gauge data is too large or too small compared to that calculated by the SC method, and also that the MIMR method performs better in terms of spatiality and accuracy than the HC and HT methods. The comparative analysis results provide a reference for the selection of discrete methods and entropy-based objectives in rain gauge network design, and provides a way to explore a more suitable rain gauge network layout scheme.

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Miandoabchi, Elnaz, and Reza Zanjirani Farahani. "Optimizing reserve capacity of urban road networks in a discrete Network Design Problem." Advances in Engineering Software 42, no. 12 (2011): 1041–50. http://dx.doi.org/10.1016/j.advengsoft.2011.07.005.

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Xing, Li Zhao, He Li Le, and Zhang Hui. "A Novel Social Network Structural Balance Based on the Particle Swarm Optimization Algorithm." Cybernetics and Information Technologies 15, no. 2 (2015): 23–35. http://dx.doi.org/10.1515/cait-2015-0026.

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AbstractExploration of the structural balance of social networks is of great importance for theoretical analysis and practical use. This study modeled the structural balance of social networks as a mathematical optimization problem by using swarm intelligence, and an efficient discrete particle swarm optimization algorithm was proposed to solve the modeled optimization problem. To take advantage of the topologies of social networks in the algorithm design, the discrete representation of the particle was redefined, and the discrete particle update principles were redesigned. To validate the efficiency of the proposed algorithm, experiments were conducted using synthetic and real-world social networks. The experiments demonstrate that the proposed algorithm not only achieves a balanced social network structure, but also automatically detects the community topology of networks.

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Sagnol, Guillaume. "Network-related problems in optimal experimental design and second order cone programming." Tatra Mountains Mathematical Publications 51, no. 1 (2012): 161–71. http://dx.doi.org/10.2478/v10127-012-0016-x.

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ABSTRACT In the past few years several applications of optimal experimental designs have emerged to optimize the measurements in communication networks. The optimal design problems arising from this kind of applications share three interesting properties: (i) measurements are only available at a small number of locations of the network; (ii) each monitor can simultaneously measure several quantities, which can be modeled by “multiresponse experiments”; (iii) the observation matrices depend on the topology of the network. We give an overview of these experimental design problems and recall recent results for the computation of optimal designs by Second Order Cone Programming (SOCP). New results for the network-monitoring of a discrete time process are presented. In particular, we show that the optimal design problem for the monitoring of an AR1 process can be reduced to the standard form and we give experimental results.

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Belotti, Pietro, and Federico Malucelli. "Network design with grooming constraints." Electronic Notes in Discrete Mathematics 17 (October 2004): 51–56. http://dx.doi.org/10.1016/j.endm.2004.03.013.

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Ljubić, Ivana, Petra Mutzel, and Bernd Zey. "Stochastic Survivable Network Design Problems." Electronic Notes in Discrete Mathematics 41 (June 2013): 245–52. http://dx.doi.org/10.1016/j.endm.2013.05.099.

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Dai, Wen Zheng. "The Design and Implementation of Key Technologies of the Computer Algorithm Dynamic System." Applied Mechanics and Materials 687-691 (November 2014): 1174–77. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1174.

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This topic mainly aimed at the dynamic simulation of some actual problems. The research is suitable for the dynamic system of modeling artificial model and learning algorithm. First it summarizes the existing network simulation model of dynamic system and network delay unit. All the feedback network and part of the feedback network model, the network topology is given and used for system simulation. The method of simulation learning algorithm and simulation instance. And then established the time-varying input and output process neural networks and network model of two kinds of discrete process neural network for its properties are analyzed and proved that the concrete learning algorithm is deduced. Finally combined with the actual problem, time-varying input and output process neural network is given in the application of system identification.

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Snoeck, André, and Matthias Winkenbach. "A Discrete Simulation-Based Optimization Algorithm for the Design of Highly Responsive Last-Mile Distribution Networks." Transportation Science 56, no. 1 (2022): 201–22. http://dx.doi.org/10.1287/trsc.2021.1105.

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Online and omnichannel retailers are proposing increasingly tight delivery deadlines, moving toward instant on-demand delivery. To operate last-mile distribution systems with such tight delivery deadlines efficiently, defining the right strategic distribution network design is of paramount importance. However, this problem exceeds the complexity of the strategic design of traditional last-mile distribution networks for two main reasons: (1) the reduced time available for order handling and delivery and (2) the absence of a delivery cut-off time that clearly separates order collection and delivery periods. This renders state-of-the-art last-mile distribution network design models inappropriate, as they assume periodic order fulfillment based on a delivery cutoff. In this study, we propose a metamodel simulation-based optimization (SO) approach to strategically design last-mile distribution networks with tight delivery deadlines. Our methodology integrates an in-depth simulator with traditional optimization techniques by extending a traditional black-box SO algorithm with an analytical model that captures the underlying structure of the decision problem. Based on a numerical study inspired by the efforts of a global fashion company to introduce on-demand distribution with tight delivery deadlines in Manhattan, we show that our approach outperforms contemporary SO approaches as well as deterministic and stochastic programming methods. In particular, our method systematically yields network designs with superior expected cost performance. Furthermore, it converges to good solutions with a lower computational budget and is more consistent in finding high-quality solutions. We show how congestion effects in the processing of orders at facilities negatively impact the network performance through late delivery of orders and reduced potential for consolidation. In addition, we show that the sensitivity of the optimal network design to congestion effects in order processing at the facilities increases as delivery deadlines become increasingly tight.

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Liang, Mian Xin. "ADACD-Based Novel Optimal Model Identification Scheme for MIMO Discrete Nonlinear Systems." Applied Mechanics and Materials 397-400 (September 2013): 1006–9. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1006.

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A model identification scheme based on the action dependent adaptive critic design (ADACD) is proposed for MIMO discrete nonlinear system. This scheme first introduces a critic network to approximate the overall identification evaluation of the system and then adjust the parameters of the identification network (i.e. action network) to minimize the output of the critic network. The system is proved to be uniformly ultimately bounded by using Lyapunov method. Furthermore, the weights of the critic and identification networks are also guaranteed to be bounded.

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Wang, Hongwei, and Chi Yu. "Design of Congestion Control Scheme for Uncertain Discrete Network Systems." International Journal of Computers Communications & Control 8, no. 6 (2013): 901. http://dx.doi.org/10.15837/ijccc.2013.6.765.

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Gao, Ziyou, Jianjun Wu, and Huijun Sun. "Solution algorithm for the bi-level discrete network design problem." Transportation Research Part B: Methodological 39, no. 6 (2005): 479–95. http://dx.doi.org/10.1016/j.trb.2004.06.004.

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Chang, Chia‐Juch, and Sheng‐Hsiung Chang. "A heuristic algorithm for solving the discrete network design problem." Transportation Planning and Technology 17, no. 1 (1993): 39–50. http://dx.doi.org/10.1080/03081069308717498.

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Mrad, Mehdi, and Mohamed Haouari. "Optimal solution of the discrete cost multicommodity network design problem." Applied Mathematics and Computation 204, no. 2 (2008): 745–53. http://dx.doi.org/10.1016/j.amc.2008.07.031.

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LIU, MEIQIN, and SENLIN ZHANG. "AN LMI APPROACH TO DESIGN H∞ CONTROLLERS FOR DISCRETE-TIME NONLINEAR SYSTEMS BASED ON UNIFIED MODELS." International Journal of Neural Systems 18, no. 05 (2008): 443–52. http://dx.doi.org/10.1142/s0129065708001701.

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A unified neural network model termed standard neural network model (SNNM) is advanced. Based on the robust L2 gain (i.e. robust H∞ performance) analysis of the SNNM with external disturbances, a state-feedback control law is designed for the SNNM to stabilize the closed-loop system and eliminate the effect of external disturbances. The control design constraints are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms (e.g. interior-point algorithms) to determine the control law. Most discrete-time recurrent neural network (RNNs) and discrete-time nonlinear systems modelled by neural networks or Takagi and Sugeno (T–S) fuzzy models can be transformed into the SNNMs to be robust H∞ performance analyzed or robust H∞ controller synthesized in a unified SNNM's framework. Finally, some examples are presented to illustrate the wide application of the SNNMs to the nonlinear systems, and the proposed approach is compared with related methods reported in the literature.

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Kotukh, Y., V. Lubchak, and O. Strakh. "New continuous-discrete model for wireless sensor networks security." Radiotekhnika, no. 210 (September 28, 2022): 99–103. http://dx.doi.org/10.30837/rt.2022.3.210.07.

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A wireless sensor network (WSN) is a group of "smart" sensors with a wireless infrastructure designed to monitor the environment. This technology is the basic concept of the Internet of Things (IoT). The WSN can transmit confidential information while working in an insecure environment. Therefore, appropriate security measures must be considered in the network design. However, computational node constraints, limited storage space, an unstable power supply, and unreliable communication channels, and unattended operations are significant barriers to the application of cybersecurity techniques in these networks. This paper considers a new continuous-discrete model of malware propagation through wireless sensor network nodes, which is based on a system of so-called dynamic equations with impulsive effect on time scales.

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Jiang, Yiming, Chenguang Yang, and Hongbin Ma. "A Review of Fuzzy Logic and Neural Network Based Intelligent Control Design for Discrete-Time Systems." Discrete Dynamics in Nature and Society 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7217364.

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Over the last few decades, the intelligent control methods such as fuzzy logic control (FLC) and neural network (NN) control have been successfully used in various applications. The rapid development of digital computer based control systems requires control signals to be calculated in a digital or discrete-time form. In this background, the intelligent control methods developed for discrete-time systems have drawn great attentions. This survey aims to present a summary of the state of the art of the design of FLC and NN-based intelligent control for discrete-time systems. For discrete-time FLC systems, numerous remarkable design approaches are introduced and a series of efficient methods to deal with the robustness, stability, and time delay of FLC discrete-time systems are recommended. Techniques for NN-based intelligent control for discrete-time systems, such as adaptive methods and adaptive dynamic programming approaches, are also reviewed. Overall, this paper is devoted to make a brief summary for recent progresses in FLC and NN-based intelligent control design for discrete-time systems as well as to present our thoughts and considerations of recent trends and potential research directions in this area.

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Parise, Francesca, and Asuman Ozdaglar. "Analysis and Interventions in Large Network Games." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (2021): 455–86. http://dx.doi.org/10.1146/annurev-control-072020-084434.

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We review classic results and recent progress on equilibrium analysis, dynamics, and optimal interventions in network games with both continuous and discrete strategy sets. We study strategic interactions in deterministic networks as well as networks generated from a stochastic network formation model. For the former case, we review a unifying framework for analysis based on the theory of variational inequalities. For the latter case, we highlight how knowledge of the stochastic network formation model can be used by a central planner to design interventions for large networks in a computationally efficient manner when exact network data are not available.

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Ríos-Rivera, Daniel, Alma Y. Alanis, and Edgar N. Sanchez. "Neural-Impulsive Pinning Control for Complex Networks Based on V-Stability." Mathematics 8, no. 9 (2020): 1388. http://dx.doi.org/10.3390/math8091388.

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In this work, a neural impulsive pinning controller for a twenty-node dynamical discrete complex network is presented. The node dynamics of the network are all different types of discrete versions of chaotic attractors of three dimensions. Using the V-stability method, we propose a criterion for selecting nodes to design pinning control, in which only a small fraction of the nodes is locally controlled in order to stabilize the network states at zero. A discrete recurrent high order neural network (RHONN) trained with extended Kalman filter (EKF) is used to identify the dynamics of controlled nodes and synthesize the control law.

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Bergman, David, Merve Bodur, Carlos Cardonha, and Andre A. Cire. "Network Models for Multiobjective Discrete Optimization." INFORMS Journal on Computing 34, no. 2 (2022): 990–1005. http://dx.doi.org/10.1287/ijoc.2021.1066.

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This paper provides a novel framework for solving multiobjective discrete optimization problems with an arbitrary number of objectives. Our framework represents these problems as network models, in that enumerating the Pareto frontier amounts to solving a multicriteria shortest-path problem in an auxiliary network. We design techniques for exploiting network models in order to accelerate the identification of the Pareto frontier, most notably a number of operations to simplify the network by removing nodes and arcs while preserving the set of nondominated solutions. We show that the proposed framework yields orders-of-magnitude performance improvements over existing state-of-the-art algorithms on five problem classes containing both linear and nonlinear objective functions. Summary of Contribution: Multiobjective optimization has a long history of research with applications in several domains. Our paper provides an alternative modeling and solution approach for multiobjective discrete optimization problems by leveraging graphical structures. Specifically, we encode the decision space of a problem as a layered network and propose graph reduction operators to preserve only solutions whose image are part of the Pareto frontier. The nondominated solutions can then be extracted through shortest-path algorithms on such a network. Numerical results comparing our method with state-of-the-art approaches on several problem classes, including the knapsack, set covering, and the traveling salesperson problem (TSP), suggest orders-of-magnitude runtime speed-ups for exactly enumerating the Pareto frontier, especially when the number of objective functions grows.

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Chen, Richard Li-Yang, and Cynthia A. Phillips. "k-Edge Failure Resilient Network Design." Electronic Notes in Discrete Mathematics 41 (June 2013): 375–82. http://dx.doi.org/10.1016/j.endm.2013.05.115.

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Benhamiche, Amal, A. Ridha Mahjoub, Nancy Perrot, and Eduardo Uchoa. "Capacitated Network Design using Bin-Packing." Electronic Notes in Discrete Mathematics 41 (June 2013): 479–86. http://dx.doi.org/10.1016/j.endm.2013.05.128.

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Gendron, Bernard. "Revisiting Lagrangian relaxation for network design." Discrete Applied Mathematics 261 (May 2019): 203–18. http://dx.doi.org/10.1016/j.dam.2018.07.003.

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Grinblat, Guillermo L., Hernán Ahumada, and Ernesto Kofman. "Quantized state simulation of spiking neural networks." SIMULATION 88, no. 3 (2011): 299–313. http://dx.doi.org/10.1177/0037549711399935.

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In this work, we explore the usage of quantized state system (QSS) methods in the simulation of networks of spiking neurons. We compare the simulation results obtained by these discrete-event algorithms with the results of the discrete time methods in use by the neuroscience community. We found that the computational costs of the QSS methods grow almost linearly with the size of the network, while they grows at least quadratically in the discrete time algorithms. We show that this advantage is mainly due to the fact that QSS methods only perform calculations in the components of the system that experience activity.

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Ogryczak, Włodzimierz, Michał Pióro, and Artur Tomaszewski. "Telecommunications network design and max-min optimization problem." Journal of Telecommunications and Information Technology, no. 3 (September 30, 2005): 43–56. http://dx.doi.org/10.26636/jtit.2005.3.326.

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Telecommunications networks are facing increasing demand for Internet services. Therefore, the problem of telecommunications network design with the objective to maximize service data flows and provide fair treatment of all services is very up-to-date. In this application, the so-called maxmin fair (MMF) solution concept is widely used to formulate the resource allocation scheme. It assumes that the worst service performance is maximized and the solution is additionally regularized with the lexicographic maximization of the second worst performance, the third one, etc. In this paper we discuss solution algorithms for MMF problems related to telecommunications network design. Due to lexicographic maximization of ordered quantities, theMMF solution concept cannot be tackled by the standard optimization model (mathematical programme). However, one can formulate a sequential lexicographic optimization procedure. The basic procedure is applicable only for convex models, thus it allows to deal with basic design problems but fails if practical discrete restrictions commonly arriving in telecommunications network design are to be taken into account. Then, however, alternative sequential approaches allowing to solve non-convex MMF problems can be used.

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Ponis, Stavros T., Angelos Delis, Sotiris P. Gayialis, Panagiotis Kasimatis, and Joseph Tan. "Applying Discrete Event Simulation (DES) in Healthcare." International Journal of Healthcare Information Systems and Informatics 8, no. 3 (2013): 58–79. http://dx.doi.org/10.4018/jhisi.2013070104.

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This paper highlights the opportunities and challenges of applying Discrete Event Simulation (DES) to support capacity planning of a network of outpatient facilities. Despite an abundance of studies using simulation techniques to examine the operation and performance of outpatient clinics, the problem of capacity allocation and planning of medical services within a network of outpatient healthcare facilities appears to be underexplored. Here, a case study of a health insurance provider that operates a network of six outpatient medical facilities in the US is used to illustrate and explore the synthesizing and adaptive, yet parsimonious nature of using DES methodology for network design and capacity planning. Results of this case study demonstrate that significant performance improvements for the network operator can be achieved with applying DES method to support the network facility capacity planning process.

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Song, Q., and M. J. Grimble. "Design of a Multivariable Neural Controller and Its Application to Gas Turbines." Journal of Dynamic Systems, Measurement, and Control 119, no. 3 (1997): 565–67. http://dx.doi.org/10.1115/1.2801295.

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The algorithm for a multivariable controller using neural network is based on a discrete-time fixed controller and the neural network provides a compensation signal to suppress the nonlinearity. The multivariable neural controller is easy to train and applied to an aircraft gas turbine plant.

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