Title :
Acoustic Fault Identification of Underwater Vehicles Based on NSOM-PNN
Author :
Luan, Ruipeng ; Ben, Kerong ; Cui, Lilin
Author_Institution :
Dept. of Comput. Eng., Naval Univ. of Eng., Wuhan, China
Abstract :
Aiming at the requirement of class incremental learning in acoustic fault identification research, a network model using a novel Self-organizing map--negative self-organizing map (NSOM) and probabilistic neural network (PNN) is proposed. The experiment of acoustic fault identification of underwater vehicle shows that the proposed network has better capability of class incremental learning than traditional PNN, and can improve the structure of network and accuracy of identification.
Keywords :
acoustic signal processing; fault diagnosis; learning (artificial intelligence); probability; self-organising feature maps; underwater sound; underwater vehicles; NSOM-PNN; acoustic fault identification; incremental learning; negative self-organizing map; probabilistic neural network; underwater vehicles; Acoustic noise; Acoustical engineering; Automotive engineering; Bayesian methods; Fault diagnosis; Neural networks; Neurons; Probability density function; Underwater acoustics; Underwater vehicles; NSOM; acoustic fault identification; pnn;
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
DOI :
10.1109/AICI.2009.224
