A novel structure which models the fuzzy inference mechanism based on neural units is proposed, to combine both the adaptive feature of neural networks and the transparency of fuzzy systems. It is shown how a perceptron with a sigmoidal activity function can perform the aggregation of premise antecedents and can thus implement conjunction or disjunction operations depending on the neuron's threshold. Knowledge-base parameters such as relevance weights of antecedents and priority weights of rules are introduced and discussed. The network topology is extracted by means of a coincidence learning law, the so-called Hebbian rule, in order to limit the problem of high dimensionality known by local classifiers. Two real-world problems are reported: Monitoring of the state of a turbocharger on the basis of model-based symptoms, and the supervision of air pressure in vehicle wheels, based on physically extracted symptoms.
