Open Access
Neural networks for combinatorial optimization
Emile H. L. Aarts,H.P. Stehouwer,J Jaap Wessels,PJ Patrick Zwietering +3 more
- 01 Jan 1994
- pp 25-40
TL;DR: The final author version and the galley proof are versions of the publication after peer review that features the final layout of the paper including the volume, issue and page numbers.
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Abstract: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
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Citations
The maximum clique problem
TL;DR: A survey of results concerning algorithms, complexity, and applications of the maximum clique problem is presented and enumerative and exact algorithms, heuristics, and a variety of other proposed methods are discussed.
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Some comparisons of complexity in dictionary-based and linear computational models
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Exponential stability criteria for a neutral type stochastic single neuron system with time-varying delays
TL;DR: Based on the linear matrix inequality (LMI) approach together with a novel Lyapunov-Krasovskii functional and stochastic analysis theory, sufficient conditions are derived to ensure that the considered system with time-varying delays is globally exponentially stable.
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Neural Network Models in Combinatorial Optimization
Mujahid N. Syed,Panos M. Pardalos +1 more
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References
Learning internal representations by error propagation
David E. Rumelhart,Geoffrey E. Hinton,Ronald J. Williams +2 more
- 01 Jan 1988
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
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The perception: a probabilistic model for information storage and organization in the brain
F. Rosenblatt
- 01 Jan 1988
TL;DR: The second and third questions are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory as mentioned in this paper.
9.3K
Neural computation of decisions in optimization problems
John J. Hopfield,David W. Tank +1 more
TL;DR: Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks.
6K
Backpropagation through time: what it does and how to do it
Paul J. Werbos
- 01 Jan 1990
TL;DR: This paper first reviews basic backpropagation, a simple method which is now being widely used in areas like pattern recognition and fault diagnosis, and describes further extensions of this method, to deal with systems other than neural networks, systems involving simultaneous equations or true recurrent networks, and other practical issues which arise with this method.
Connectionist Models and Their Properties
TL;DR: A general connectionist model is introduced and how it might be used in cognitive science is considered, among the issues addressed are: stability and noise-sensitivity, distributed decision-making, time and sequence problems, and the representation of complex concepts.
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