A continuous-time inference network and its hybrid implementations
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AbstractA class of binary relation inference network has been recently proposed for applications in graph (or network) optimization and in timing analysis of microprocessor systems. In handling the timing consistency problem between different events, there are some intrinsic weaknesses underlying this type of discrete-time inference network; namely, network instability and oscillation under specific circumstances, and the slow convergence rate commonly observed in large networks. To circumvent the potential shortcomings of existing inference networks, state-space techniques are used to derive a more robust continuous-time inference network. Simulation studies on two hybrid schemes indicate significant improvements over discrete-time inference network, and demonstrate their practical viability for applications in time-varying cases.
All Author(s) ListLam KP
Journal nameInternational Journal of Systems Science
Volume Number27
Issue Number12
Pages1425 - 1433
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesAutomation & Control Systems; AUTOMATION & CONTROL SYSTEMS; Computer Science; Computer Science, Theory & Methods; COMPUTER SCIENCE, THEORY & METHODS; Operations Research & Management Science; OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Last updated on 2020-26-03 at 02:55