A Neural Network Aided Filtering Algorithm for Diagnosing and Predicting Nonlinear Contaminant Transport Dynamics

Author:

Nonaka M.,Thomas N. H.

Publisher

Springer Netherlands

Reference27 articles.

1. Nonaka, M. and Thomas, N. H.: Kalman filter method for statistical modelling of contaminant transport: An outline of the methodology, in Proc. of the Sixth Int. Conference on Stochastic Hydraulics, pp. 647–654, Taipei, 1992.

2. Nonaka, M. and Thomas, N. H.: Enhanced Kalman filter method for estimating statistical fields of transport dynamics-l. Mathematical basis, J. Min. Material Process. Inst. Japan, 112 (1996), 89–93.

3. Simutis, R., Havlik, I. and Luebbert, A.: A process state estimation and prediction in a production-scale beer fermentation using fuzzy aided extended Kalman filter and neural networks, in IFAC Symposium Series No. 10,.pp. 95–100, Pergamon Press, New York, 1992.

4. Watanabe, K., Fukuda, T. and Tzafestas, S. G.: Learning algorithm of layered neural networks via extended Kalman filter, Int J Systems Science, 22 (1991), 753–768.

5. Iiguni, Y., Sakai, H. and Tokumaru, H.: A real-time learning algorithm for a multilayered neural network based on the Extended Kalman filter, IEEE Trans Signal Process, 40 (1992), 959–966.

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