Simulation metamodelling with neural networks: An experimental investigation

A Simulation Metamodelling Based Neural Networks for Lot-sizing Problem in Mto Sector

Simulation is essentially a trial-and-error approach, and is therefore, time-consuming and does not provide a method for optimization. Metamodelling techniques have been recently pursued in order to tackle these drawbacks. The main objective has been to provide robust, fast decision support aids to enhance the overall effectiveness of decision-making processes. This paper proposes an applicatio...

An experimental comparison of recurrent neural networks

Many different discrete-time recurrent neural network architectures have been proposed. However, there has been virtually no effort to compare these arch:tectures experimentally. In this paper we review and categorize many of these architectures and compare how they perform on various classes of simple problems including grammatical inference and nonlinear system identification.

Cooperative Co-Evolving Neural Networks for Robosoccer Simulation

Sto hasti simulation with neural networks

Regression Metamodelling in Simulation Using Bayesian Methods

This paper further develops some of the ideas set out by Cheng (1998) for output analysis using Bayesian Markov Chain Monte Carlo (MCMC) techniques, when a regression metamodel is to be fitted to simulation output. The particular situation addressed by Cheng was where there is uncertainty about the number of parameters needed to specify a model. This arises because there may be uncertainty abou...