Control of autonomous robot behavior using data filtering through adaptive resonance theory

The aim of the article is to use neural networks to control autonomous robot behavior. The type of the controlling neural network was chosen a backpropagation neural network with a sigmoidal transfer function. The focus in this article is put on the use adaptive resonance theory (ART1) for data filtering. ART1 is used for preprocessing of the training set. This allows finding typical patterns in the full training set and thus covering the whole space of solutions. The neural network adapted by a reduced training set has a greater ability of generalization. The work also discusses the influence of vigilance parameter settings for filtering the training set. The proposed approach to data filtering through ART1 is experimentally verified to control the behavior of an autonomous robot in an unknown environment with varying degrees of difficulty regarding the location of obstacles. All obtained results are evaluated in the conclusion.

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