Analyzing the Benefits of Using a Fuzzy-Neuro Model in the Accuracy of the NeurAge System: an Agent-Based System for Classification Tasks

The use of intelligent agents in the structure of multi-classifier systems has been investigated in order to overcome some drawbacks of these systems and, as a consequence, to improve the performance of such systems. As a result of this, the NeurAge system was proposed. This system is composed by several neural agents which communicate (negotiate) a common result for the testing patterns. The NeurAge system has been successfully applied in some classification tasks. Basically, in these investigations, NeurAge has used multi-layer perceptrons (MLPs) as the neural network module of its agents. In this paper, it is presented an investigation of the use of the NeurAge system using other types of classifiers, mainly fuzzy MLP. The main aim of this investigation is to analyze the benefits of using fuzzy neural networks in the performance of the NeurAge system.