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Author Silva, M.P. ♦ Cardeira, C. ♦ Mammeri, Z.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1997
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Neural networks ♦ Hopfield neural networks ♦ Scheduling algorithm ♦ Processor scheduling ♦ Neural network hardware ♦ Physics computing ♦ Computer simulation ♦ Artificial neural networks ♦ Parallel architectures ♦ Actuators
Abstract Real-time applications are increasingly becoming more complex, leading to the necessary development of fast scheduling algorithms. Therefore, the use of algorithms with a parallel search of feasible schedules seems to be attractive. In turn, Hopfield-type neural networks are suitable to solve complex combinatorial problems, owing to their fast convergence, if analog hardware is implemented. However, these neural networks have associated concepts of sub-optimality and the possibility of unfeasible solutions, which are contrary to the notion of system predictability. The paper presents a systematic procedure to map the scheduling problem onto a neural network in such a way that network solutions are always feasible schedules. Network convergence time is studied with digital computer simulations, using a discrete time model. Global asymptotic consistency between the discrete time model and the continuous one is assured. The paper also presents an analysis of the complexity of the proposed method.
Description Author affiliation: IDMEC/IST, Lisbon, Portugal (Silva, M.P.)
ISBN 0818681292
ISSN 10896503
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1997-09-01
Publisher Place Hungary
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 687.56 kB
Page Count 8
Starting Page 671
Ending Page 678

Source: IEEE Xplore Digital Library