Title :
A SOM-based probabilistic neural network for classification of ship noises
Author :
Chen, Jie ; Li, Haiying ; Tang, Shiwei ; Sun, Jincai
Author_Institution :
Nat. Lab. on Machine Perception in Center for Inf. Sci., Peking Univ., Beijing, China
fDate :
29 June-1 July 2002
Abstract :
A probabilistic neural network (PNN) is applied to the classification of ship noises for its simplicity in the training process. However, a main limitation of PNNs is that all computations are carried out at runtime, and it may become an overburden if the training set is large. This paper presents a modified PNN algorithm, based on self-organizing maps (SOM), which can reduce the running time through real-time optimization of the training set, and retains the virtue of the training procedure as a simple forward computation at the same time. Experimental results verifying the proposed algorithm are provided.
Keywords :
classification; feature extraction; learning (artificial intelligence); marine systems; optimisation; probabilistic automata; self-organising feature maps; ships; underwater sound; SOM-PNN algorithms; SOM-based probabilistic neural networks; forward computation; neural network training process; self-organizing maps; ship noise classification; ship noise feature extraction; training set optimization; training set size; vessel classification; Density functional theory; Information processing; Information science; Kernel; Laboratories; Marine vehicles; Neural networks; Process control; Runtime; Self organizing feature maps;
Conference_Titel :
Communications, Circuits and Systems and West Sino Expositions, IEEE 2002 International Conference on
Print_ISBN :
0-7803-7547-5
DOI :
10.1109/ICCCAS.2002.1179000
