Multiple Neural Networks System for Dynamic Environments

We propose a ¿multiple neural networks¿ system for dynamic environments, where one or more neural nets may no longer be able to properly operate, due to sensible partial changes in the characteristics of the individuals. We assume that each expert network has a reliability factor that can be dynamically re-evaluated on the ground of the global recognition operated by the overall group. Since the net´s ¿degree of reliability¿ is defined as ¿the probability that the net is giving the desired output¿, in case of conflicts between the outputs of the various nets the re-evaluation of their ¿degrees of reliability¿ can be simply performed on the basis of the Bayes Rule. The new vector of reliability will be used for making the final choice, by applying the ¿inclusion based¿ algorithm over all the maximally consistent subsets of the global outcome. Finally, the nets recognized as responsible for the conflicts will be automatically forced to learn about the changes in the individuals´ characteristics and avoid to make the same error in the immediate future.