Equivalent aspects of neural networks and fuzzy logic control

Neural networks and fuzzy logic are two separate structures which have each been used to control complex nonlinear systems. Each method possesses certain key attributes which provide attractive design features. Recently methods have evolved which combine the best of both methods-automatic learning from input/output data for the neural nets and interpolation between expert-system type rules for fuzzy logic. In this paper we show an equivalence between the two diverse methods