Detection of gas leakage sound using modular neural networks for unknown environments

Neurocomputing

Volumn 62, Issue 1-4, 2004, Pages 427-440

  Kotani, Manabu ^a   Katsura, Masanori ^a   Ozawa, Seiichi ^a  

^a KOBE UNIVERSITY   (Japan)

Author keywords

Acoustic diagnosis; Gas leakage; Modular neural networks; Radial basis function networks; Sound

Indexed keywords

CHEMICAL PLANTS; CRACKS; GASES; LEAKAGE (FLUID); PETROLEUM REFINING;

ACOUSTIC DIAGNOSIS TECHNIQUES; ARTIFICIAL GASES; GAS LEAKAGES;

NEURAL NETWORKS;

PETROLEUM;

ACOUSTICS; ADAPTATION; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; DIAGNOSTIC PROCEDURE; DYNAMICS; ENVIRONMENT; EXPERIMENTATION; FOURIER TRANSFORM MASS SPECTROMETRY; GAS; MATHEMATICAL COMPUTING; NOISE MEASUREMENT; PETROCHEMICAL INDUSTRY; PRIORITY JOURNAL; SOUND DETECTION;

EID: 8644243811     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2004.06.002     Document Type: Article

References (14)

1. R. Anand K. Mehrotra C.K. Mohan S. Ranka Efficient classification for multiclass problem using modular neural networks IEEE Trans. Neural Networks 6 1 1995 117-124
2. M.H. Hassoun, Fundamentals of Artificial Neural Networks, MIT Press, Cambridge, MA, 1995, pp. 286-301
3. S. Haykin T.K. Bhattacharya Modular learning strategy for signal detection in a nonstationary environment IEEE Trans. Signal Process. 45 6 1997 1619-1637
4. R.A. Jacobs M.I. Jordan S.J. Nowlan G.E. Hinton Adaptive mixtures of local experts Neural Comput. 3 1991 79-97
5. M. Kotani T. Miyata K. Akazawa Acoustic diagnosis for gas leakage in pipes under noisy environments Proceedings of IMEKO-XV, vol. VIII 1999 55-60
6. S. Lee R.M. Kil Multilayer feedforward potential function network Proceedings of International Conference on Neural Network 1988 161-171
7. B. Lu M. Ito Task decomposition and module combination based on class relations: A modular neural network for pattern classification IEEE Trans. Neural Networks 10 5 1999 1244-1256
8. J. Moody C. Darken Learning with localized receptive field Proceedings of 1988 Connectionist Models Summer School 1988 133-143
9. J. Park J.W. Sandberg Universal approximation using radial basis functions network Neural Comput. 3 1991 246-257
10. T. Poggio F. Girosi A Theory of Neural Networks for Approximation and Learning, A. I. Memo 1140 1989 MIT Press Cambridge, MA
11. C. Rodriguez S. Rementeria J.I. Martin A. Lafuente J. Muguerza J. Prez A modular neural network approach to fault diagnosis IEEE Trans. Neural Networks 7 2 1996 326-340
12. D.E. Rumelhart G.E. Hinton R.J. Williams Learning internal representations by error propagation Parallel Distributed Processing Explorations in the Microstructure of Cognition, vol. I 1986 MIT Press Cambridge, MA 318-362
13. A.J.C. Sharkey, Combining Artificial Neural Nets Ensemble and Modular Multi-Net Systems, Springer, Berlin, 1998, pp. 1-30
14. P.V. Yee S. Haykin Regularized radial-basis function networks: Theory and applications, Adaptive and Learning Systems for Signal Processing, Communications and Control 2001 Wiley New York