Multistep parameter learning in a neural network based fuzzy diagnosis module

This paper introduces an improved method for optimizing parameters of an neural network based fuzzy diagnosis module. With the specific structure of a conventional fuzzy system the diagnosis module is used for the linguistic qualification of continuous signals to detect faulty components in technical processes. The design process of the module structure itself is based on numerical methods applied for neural networks. Training data indicating various system states delivered by a distributed continuous simulator are used to set up the initial module network structure. The proposed multistep parameter learning method enables fast adaptation of the diagnosis module parameters by avoiding mutual influences of parameters during the learning phase and consideration of individual parameter learning characteristics.