基于增量学习的RBF神经网络的噪声源识别

Acoustic Fault Identification Based on the RBF Neural Network for Incremental Learning

本文提出了一种用于水下航行器噪声源识别的RBF模糊神经网络模型。该模型采用PCM聚类算法并具有增量学习能力,网络输出节点在线可调,保证了网络具有较高的泛化能力和一定的学习新故障模式的能力。仿真结果表明,该模型是有效的。

A RBF fuzzy neural network for targeting the characteristics of acoustic fault sources of underwater vehicles is presented. The neural network uses the possibilistic C-means（PCM） clustering algorithm. 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 is efficient.