Pattern classification by an incremental learning fuzzy neural network

Abstract

A new learning algorithm suitable for pattern classification in machine condition health monitoring based on fuzzy neural networks called an "incremental learning fuzzy neuron network" (ILFN) has been developed. The ILFN, using Gaussian neurons to represent the distributions of the input space, is an online one-pass incremental learning algorithm. The network is a self-organized classifier with the ability to adaptively learn new information without forgetting old knowledge. To prove the concept the simulations have been performed with the vibration data known as Westland vibration data set. Furthermore, the classification performance of the network has been tested on other benchmark data sets, such as Fisher's iris data (1936) and a vowel data set. For the generalization capability, comparison studies among other well-known classifiers were performed and the ILFN was found competitive with or even superior to many existing classifiers. Additionally the ILFN uses far less training time than conventional classifiers.