Estimation of particulate loading in diesel particulate filter by using self structured neural network

JSME International Journal. Series C: Mechanical Systems, Machine Elements and Manufacturing

Volumn 42, Issue 2, 1999, Pages 335-343

  Bohari, Abdul Rahman ^a   Mizuno, Naoki ^a  

^a NAGOYA INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Diesel Particulate Filter; Estimation; Filter Dynamics; Genetic Algorithm; Neural Network; Nonlinear System; Training and Structure Design Algorithm

Indexed keywords

DIESEL FUELS; ESTIMATION; GENETIC ALGORITHMS; NEURAL NETWORKS; NONLINEAR SYSTEMS; PARTICULATE EMISSIONS;

DIESEL PARTICULATE FILTERS; SELF STRUCTURED NEURAL NETWORKS;

FUEL FILTERS;

DIESEL ENGINE; FILTER; GAS-SOLID SEPARATION; NEURAL NETWORK;

EID: 0033382482     PISSN: 13447653     EISSN: None     Source Type: Journal    
DOI: 10.1299/jsmec.42.335     Document Type: Article

References (18)

1. Sakai, Y., After-treatment for Exhaust Gas Emission Reduction, Journal of JSME, (in Japanese), Vol. 95, No. 882 (1992), p. 405.
2. Hirai, H., Suto, H. and Hori, M., Estimation of Paniculate Loading in Diesel Particulate Filter Adapted to Driving Condition, Automobile Research, (in Japanese), Vol 15, No. 2 (1993), p. 20.
3. Walton, F.B., Archambault, P. and Legiehn, M., A One-point Calibration Method for the On-line Measurement of Diesel Particulate Loading in Ceramics Filters, SAE Paper 930366 (1993), p. 175.
4. Bohari, A.R. and Mizuno, N., Estimation of Particulate Loading in Diesel Particulate Filter Using Neural Network, JSME International Journal, Series C, Vol. 41, No. 4 (1998), p. 792.
5. Karayiannis, N.B. and Venetsnopoulos, A.N., Artificial Neural Networks, (1992), Kluwer Academic Publisher.
6. Michalewicz, Z., Genetic Algorithms+Data Structures= Evolution Program, (1992), Springer Verlag.
7. Mitchell, M., An Introduction to Genetic Algorithms, (1996), MIT Press.
8. Kitano, H., Empirical Studies on the Speed of Convergence of Neural Network Training Using Genetic Algorithms, Proc. of AAAI-90, (1990), p. 789.
9. Kolen, J.F. and Pollack, J.B., Backpropagation is Sensitive to Initial Conditions, Neural Information Processing System 3, ed. by R.P. Lipmann et. al. (1991), p. 860, Morgan Kaufmann Publishers.
10. Kobayashi, M., Yamaguchi, T. and Hirai, H., Fast Learning Algorithm for Layered Neural Networks Based on Adaptive Algorithm Method, Preprint of the 35th Japan Joint Automatic Control Conference, (in Japanese), No. 3008 (1992), p. 389.
11. Scalero, R.S. and Tepedelenlioglu, N., A Fast New Algorithm for Training Feedforward Neural Networks, IEEE Transaction on Signal Processing, Vol. 40, No. 1 (1992), p. 202.
12. Shinji, S., Adaptive Algorithm, (in Japanese), (1990), Sangyo Tosho.
13. Kitano, H., Evolution of Metabolism for Morphogenesis, Artificial Life IV, ed. By R.A. Brooks and P. Maes, (1994), p. 49.
14. Nagao, T., Agui, T. and Nagahashi, H., Structural Evolution of Neural Networks Having Arbitrary Connections Using a Genetic Method, The Technical Reports of The Institute of Electronics, Information, and Communication Engineers, (In Japanese), Vol. 92, No. 107 (1992), p. 37.
15. Kitano, H., Neurogenetic Learning: an Integrated Way of Designing and Training Neural Networks Using Genetic Algorithms, Physica D 75 (1994), p. 225.
16. Kurita, T., A Method to Determine the Number of Hidden Units of Three Layered Neural Networks by Information Criteria, The Transactions of The Institute of Electronics, Information, and Communication Engineers, (In Japanese), D-II, Vol. J73-D-II, No. 11 (1990), p. 1872.
17. Akaike, H., A New Look at the Statistical Model Identification, IEEE Transaction on Automatic Control, Vol. AC-19, No. 6 (1974), p. 716.
18. Rissanen, J., Stochastic Complexity and Modeling, The Annals of Statistics, Vol. 14, No. 3 (1986), p. 1080.