2011
EMSS 2011 Proceeding
A New Devs-Based Generic Artficial Neural Network Modeling Approach
Authors: Samuel Toma, Laurent Capocchi, Dominique Federici
Abstract
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 a system or a 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.
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