System identification: theory for the user
System identification: theory for the user
Advances in neural information processing systems 2
Multi-layered feedforward neural networks for image segmentation
Multi-layered feedforward neural networks for image segmentation
A pruning method for the recursive least squared algorithm
Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Effect of pruning and early stopping on performance of a boosting ensemble
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Ranking a random feature for variable and feature selection
The Journal of Machine Learning Research
Feedforward Neural Network Construction Using Cross Validation
Neural Computation
Neural Computing and Applications
A Stochastic Algorithm for Feature Selection in Pattern Recognition
The Journal of Machine Learning Research
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters
The Journal of Machine Learning Research
Neural Modeling of an Induction Furnace Using Robust Learning Criteria
Integrated Computer-Aided Engineering
Simulation Reduction Models Approach Using Neural Network
UKSIM '08 Proceedings of the Tenth International Conference on Computer Modeling and Simulation
Pruned neural networks for regression
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
A new pruning heuristic based on variance analysis of sensitivity information
IEEE Transactions on Neural Networks
Neural-network construction and selection in nonlinear modeling
IEEE Transactions on Neural Networks
A node pruning algorithm based on a Fourier amplitude sensitivity test method
IEEE Transactions on Neural Networks
Performing Feature Selection With Multilayer Perceptrons
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Simulation is a useful tool for the evaluation of a Master Production/Distribution Schedule (MPS). The goal of this paper is to propose a new approach to designing a simulation model by reducing its complexity. According to the theory of constraints, a reduced model is built using bottlenecks and a neural network exclusively. This paper focuses on one step of the network model design: determining the structure of the network. This task may be performed by using the constructive or pruning approaches. The main contribution of this paper is twofold; it first proposes a new pruning algorithm based on an analysis of the variance of the sensitivity of all parameters of the network and then uses this algorithm to reduce the simulation model of a sawmill supply chain. In the first step, the proposed pruning algorithm is tested with two simulation examples and compared with three classical pruning algorithms fromthe literature. In the second step, these four algorithms are used to determine the optimal structure of the network used for the complexity-reduction design procedure of the simulation model of a sawmill supply chain.