A NEW DEVS-BASED GENERIC ARTFICIAL NEURAL NETWORK MODELING APPROACH

Samuel Toma (1) , Laurent Capocchi (2) , Dominique Federici (2)

1 TIC
2 SPE - Sciences pour l'environnement

The Artificial Neural Network (ANN) is a black box model capable of resolving paradigms that linear computing cannot. Therefore, the configuration of ANN is a hard task for modeler since it depends on the application complexity. The Discrete EVent system Specification (DEVS) is a formalism to describe discrete event system in a hierarchical and modular way. DEVS is mainly used to defragment system or model in an easy way allowing the interaction with the architecture and behavior of the system. This paper presents a new artificial neural network modeling approach using DEVS formalism in order to facilitate the network configuration by introducing a new scheme of the training phase. We validate our approach with a simple not linearly separable data set example provided by two-dimensional XOR problem.

Mots clés: discrete event systems; artificial neural networks; artificial intelligence; simulation; learning systems; modeling; simulation.

Domaines: Modélisation et simulation

Samuel Toma, Laurent Capocchi, Dominique Federici. A NEW DEVS-BASED GENERIC ARTFICIAL NEURAL NETWORK MODELING APPROACH. The 23rd European Modeling & Simulation Symposium (Simulation in Industry), Sep 2011, Rome, Italy. ⟨hal-00603736⟩