Boolean factors based Artificial Neural Network (ouvre dans un nouvel onglet)

Résumé

Due to its ability to solve nonlinear problems, Artificial Neural Network (ANN) could be applied in several areas of life. However, defining its architecture for solving a given problem is not formalized and remains an open research problem. On the other hand the complexity of such a technique due to its “black box” aspect, makes its interpretation more tedious. Since optimal factors completely cover the data and therefore give an explanation to these data, we propose in this paper to build feedforward ANNs using the optimal factors obtained from the boolean context representing a data. We show through experiments and comparisons on the use datasets that this approach provides relatively better results than those existing in the literature.

Mots-clés

• #neural networks and applications
• #fuzzy logic and control systems
• #metaheuristic optimization algorithms research
• #artificial neural network
• #computer science
• #context (archaeology)
• #feedforward neural network
• #artificial intelligence
• #boolean function
• #cover (algebra)
• #black box
• #machine learning
• #algorithm
• #engineering