FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2153846688> ?p ?o ?g. }

• W2153846688 endingPage "408" @default.
• W2153846688 startingPage "397" @default.
• W2153846688 abstract "Consider network topological optimization under a reliability constraint. The objective is to find the topological layout of links, at minimal cost, under the constraint: all-terminal network reliability is not less than a given level of system reliability. The all-terminal reliability is Pr{every pair of nodes in the network can communicate with each other}. This paper presents a new approach based on artificial neural networks (ANN) for solving the problem. The problem is mapped onto an optimization ANN (OPTI-net) by constructing an energy function whose minimization process drives the neural network into one of its stable states. This stable state corresponds to a solution for the network design problem. The OPTI-net favors states that correspond to a selection of links with an overall reliability greater than or equal to a threshold value. Among these states it also favors the one which has the lowest total cost. Hysteresis McCulloch-Pitts neuron model is used in the solution, due to its performance and fast convergence. Considering the NP-hard complexity of the exact reliability calculation, together with the iterative behavior of the neural networks, bounds for the all-terminal reliability are used. This paper introduces new upper and lower bounds that are functions of the link selection and uses them to represent the network reliability. The neural network is tested via computer simulation using three problem sets. The first two sets are used to compare the results obtained by this method to those obtained by previous heuristics. The third test set contains five networks of larger sizes for which no results have been reported by previous methods. This paper rinds the optimal or near-optimal solutions for most of the problems in a relatively short time. The OPTI-net found many good solutions for a 50-vertex 1225-arc network in 1/2 CPU hour. For each problem instance, many solutions are found at each run of the simulator. The strengths of this neural network approach are very slowly increasing computation time with respect to network size, effective optimization, and flexibility. The OPTI-net is very effective in identifying optimal, or suboptimal, solutions even in search spaces up to /spl ap/10/sup 16/ for a fully connected network with 50 vertexes. The OPTI-net is the first approach to be applied on such large networks. The simulation results show that the neural approach is more efficient in designing networks of large sizes compared to other heuristic techniques." @default.
• W2153846688 created "2016-06-24" @default.
• W2153846688 creator A5023431123 @default.
• W2153846688 creator A5048290387 @default.
• W2153846688 date "2001-01-01" @default.
• W2153846688 modified "2023-09-23" @default.
• W2153846688 title "A neural approach to topological optimization of communication networks, with reliability constraints" @default.
• W2153846688 cites W1531840750 @default.
• W2153846688 cites W1583541829 @default.
• W2153846688 cites W1972312685 @default.
• W2153846688 cites W1985466427 @default.
• W2153846688 cites W2008084054 @default.
• W2153846688 cites W2010828888 @default.
• W2153846688 cites W2041498661 @default.
• W2153846688 cites W2044456631 @default.
• W2153846688 cites W2055010035 @default.
• W2153846688 cites W2081177026 @default.
• W2153846688 cites W2082620437 @default.
• W2153846688 cites W2110979698 @default.
• W2153846688 cites W2115800482 @default.
• W2153846688 cites W2119750490 @default.
• W2153846688 cites W2120911818 @default.
• W2153846688 cites W2128110222 @default.
• W2153846688 cites W2129904182 @default.
• W2153846688 cites W2135195639 @default.
• W2153846688 cites W2136087206 @default.
• W2153846688 cites W2136479615 @default.
• W2153846688 cites W2137896257 @default.
• W2153846688 cites W2153251922 @default.
• W2153846688 cites W2157332065 @default.
• W2153846688 cites W2164708606 @default.
• W2153846688 cites W2484539289 @default.
• W2153846688 cites W4250205649 @default.
• W2153846688 cites W4250574621 @default.
• W2153846688 doi "https://doi.org/10.1109/24.983401" @default.
• W2153846688 hasPublicationYear "2001" @default.
• W2153846688 type Work @default.
• W2153846688 sameAs 2153846688 @default.
• W2153846688 citedByCount "47" @default.
• W2153846688 countsByYear W21538466882012 @default.
• W2153846688 countsByYear W21538466882013 @default.
• W2153846688 countsByYear W21538466882014 @default.
• W2153846688 countsByYear W21538466882015 @default.
• W2153846688 countsByYear W21538466882016 @default.
• W2153846688 countsByYear W21538466882018 @default.
• W2153846688 countsByYear W21538466882019 @default.
• W2153846688 countsByYear W21538466882020 @default.
• W2153846688 countsByYear W21538466882021 @default.
• W2153846688 countsByYear W21538466882022 @default.
• W2153846688 countsByYear W21538466882023 @default.
• W2153846688 crossrefType "journal-article" @default.
• W2153846688 hasAuthorship W2153846688A5023431123 @default.
• W2153846688 hasAuthorship W2153846688A5048290387 @default.
• W2153846688 hasConcept C105795698 @default.
• W2153846688 hasConcept C114614502 @default.
• W2153846688 hasConcept C121332964 @default.
• W2153846688 hasConcept C126255220 @default.
• W2153846688 hasConcept C127705205 @default.
• W2153846688 hasConcept C137836250 @default.
• W2153846688 hasConcept C154945302 @default.
• W2153846688 hasConcept C163164238 @default.
• W2153846688 hasConcept C163258240 @default.
• W2153846688 hasConcept C184720557 @default.
• W2153846688 hasConcept C201729545 @default.
• W2153846688 hasConcept C2524010 @default.
• W2153846688 hasConcept C2776036281 @default.
• W2153846688 hasConcept C2779664074 @default.
• W2153846688 hasConcept C33923547 @default.
• W2153846688 hasConcept C41008148 @default.
• W2153846688 hasConcept C43214815 @default.
• W2153846688 hasConcept C50644808 @default.
• W2153846688 hasConcept C62520636 @default.
• W2153846688 hasConcept C76155785 @default.
• W2153846688 hasConceptScore W2153846688C105795698 @default.
• W2153846688 hasConceptScore W2153846688C114614502 @default.
• W2153846688 hasConceptScore W2153846688C121332964 @default.
• W2153846688 hasConceptScore W2153846688C126255220 @default.
• W2153846688 hasConceptScore W2153846688C127705205 @default.
• W2153846688 hasConceptScore W2153846688C137836250 @default.
• W2153846688 hasConceptScore W2153846688C154945302 @default.
• W2153846688 hasConceptScore W2153846688C163164238 @default.
• W2153846688 hasConceptScore W2153846688C163258240 @default.
• W2153846688 hasConceptScore W2153846688C184720557 @default.
• W2153846688 hasConceptScore W2153846688C201729545 @default.
• W2153846688 hasConceptScore W2153846688C2524010 @default.
• W2153846688 hasConceptScore W2153846688C2776036281 @default.
• W2153846688 hasConceptScore W2153846688C2779664074 @default.
• W2153846688 hasConceptScore W2153846688C33923547 @default.
• W2153846688 hasConceptScore W2153846688C41008148 @default.
• W2153846688 hasConceptScore W2153846688C43214815 @default.
• W2153846688 hasConceptScore W2153846688C50644808 @default.
• W2153846688 hasConceptScore W2153846688C62520636 @default.
• W2153846688 hasConceptScore W2153846688C76155785 @default.
• W2153846688 hasIssue "4" @default.
• W2153846688 hasLocation W21538466881 @default.
• W2153846688 hasOpenAccess W2153846688 @default.
• W2153846688 hasPrimaryLocation W21538466881 @default.
• W2153846688 hasRelatedWork W1544374784 @default.

• next