The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
An HLA-based multiagent system for optimized resource allocation after strong earthquakes
Proceedings of the 38th conference on Winter simulation
Modeling and Simulation for Material Supply in Emergent Disaster Based on Agent-DEVS/HLA
ICIC '10 Proceedings of the 2010 Third International Conference on Information and Computing - Volume 04
Simulating distribution of emergency relief supplies for disaster response operations
Winter Simulation Conference
| Hi-index | 0.00 |
Modeling material supply in emergent disasters has become an effective means to foster emergency relief, for which Agent-DEVS (discrete event system specification) model parameter optimization is important. Based on fundamental support vector machine (SVM) principles, a parameter optimization flow for an Agent-DEVS model is put forward, and a parameter optimization model for the supply task parameter in simulation models for material supply in an emergent disaster is established. Some key techniques, including data extraction and preprocessing, kernel function selection and SVM model parameter preferences, are analyzed, and the comparison with the back-propagation neural network is examined. A simulation test shows that SVM has strong learning and fitting capabilities and weak dependence on samples. It enhances the dynamics of Agent-DEVS models. The self-learning ability significantly improves model intelligence and the optimized parameters provide models with more elaborate descriptive abilities.