On the relationship between neural networks and fuzzy reasoning

In this paper we investigate the relationship between fuzzy logic and neural networks. We show that fuzzy reasoning with general reasoning operators can be mapped on a multilayer perceptron architecture. This mapping allows us to give physical meaning to the weights of the neural network and may allow the user to bypass the training stage of the network if the necessary information is available. These ideas are exemplified with the help of a real problem from geography, concerned with the assessment of the regeneration potential of a burned forest for the optimal allocation of reforestation resources.