A multiple network architecture combined by fuzzy integral

Recently, in the area of artificial neural network, the concept of combining multiple networks has been proposed as a new direction for the development of highly reliable neural network systems. In this paper we propose a method for multinetwork combination based on the fuzzy integral. This technique nonlinearly combines objective evidence in the form of a fuzzy membership function with subjective evaluation of the worth of the individual neural networks with respect to the decision. The experimental results with the recognition problem of online handwriting characters show that the performance of individual networks could be improved significantly.