Journal Article10.1016/J.CIE.2007.08.012
Neural network-based simulation metamodels for predicting probability distributions
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TL;DR: A series of tests demonstrate the ability of the neural networks to capture the behavior of the underlying systems and to represent the inherent uncertainty with a reasonable degree of accuracy in a stochastic simulation model.
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About: This article is published in Computers & Industrial Engineering. The article was published on 01 May 2008. The article focuses on the topics: Systems simulation & Stochastic neural network.
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Citations
A prediction interval-based approach to determine optimal structures of neural network metamodels
TL;DR: This research aims at adopting a technique for constructing prediction intervals for point predictions of neural network metamodels through a more than 77% reduction in number of potential candidates, and optimal structure for neural networks is found in a manageable time.
106
Load Forecasting and Neural Networks: A Prediction Interval-Based Perspective
Abbas Khosravi,Saeid Nahavandi,Doug Creighton +2 more
- 01 Jan 2010
TL;DR: To objectively and comprehensively assess quality of constructed prediction intervals, a new index based on length and coverage probability of prediction intervals is developed.
32
An integrated neural network–simulation algorithm for performance optimisation of the bi-criteria two-stage assembly flow-shop scheduling problem with stochastic activities
TL;DR: This paper presents an integrated computer simulation and Artificial Neural Network algorithm for a stochastic Two-Stage Assembly Flow-Shop Scheduling Problem (TSAFSP) with setup times under a weighted sum of makespan and mean completion time (MCT) criteria, known as bi-criteria.
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A flexible artificial neural network–fuzzy simulation algorithm for scheduling a flow shop with multiple processors
TL;DR: This is the first study that introduces an intelligent and flexible approach for handling imprecision and nonlinearity of scheduling problems in flow shops with multiple processors.
23
Ordinary Kriging metamodel-assisted Ant Colony algorithm for fast analog design optimization
Oghenekarho Okobiah,Saraju P. Mohanty,Elias Kougianos +2 more
- 19 Mar 2012
TL;DR: This paper explores an ordinary Kriging based metamodeling technique that allows designers to create a model of a circuit with very good accuracy, while greatly reducing the time required for simulations.
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Simon Haykin
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Simulation Modeling and Analysis
Averill M. Law,W. David Kelton +1 more
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TL;DR: The text is designed for a one-term or two-quarter course in simulation offered in departments of industrial engineering, business, computer science and operations research.
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Discrete-Event System Simulation
Jerry Banks,John S. Carson,Barry L. Nelson,David M. Nicol +3 more
- 21 Sep 1995
TL;DR: Beleska o autorima: str. XV-XVI. as mentioned in this paper - Bibliografija uz svako poglavlje. - Registar.
Discrete Event System Simulation
J. Vijayarangam,Shanmugasundaram Kamalakannan,R. Priya +2 more
- 04 Mar 2024
TL;DR: Discrete event simulation is a powerful technique for modelling and analyzing complex systems, particularly those where analytical solutions are impractical. It is a flexible and adaptable discipline within computer science that finds extensive application in various fields.
1K