RBF NETWORK BASED ON FUZZY CLUSTERING ALGORITHM FOR ACOUSTIC FAULT IDENTIFICATION OF UNDERWATER VEHICLES

摘要

According to the characteristics of acoustic fault sources of underwater vehicles, a novel sources detection model using radial basis function (RBF) neural network based on fuzzy clustering algorithm is proposed. The extended fuzzy c-means (FCM) clustering algorithm is utilized to determine the number of hidden neurons, especially, the output layer neurons can be modified on-line so that the network has the capability of incremental learning. An example of diagnosis indicates that the proposed neural network diagnosis system can detect and recognize new faults, and can learn incrementally.