Abstract
In this paper a new method of neural filtering using artificial neural network systems is presented for the filtering problems of linear and nonlinear, stationary and nonstationary stochastic signals. The neural filter (denoted neurofilter) developed in this paper has either finite impulse response (FIR) structure or infinite impulse response (IIR) structure. The neurofilter differs from the conventional linear digital FIR and IIR filters because the artificial neural network system used in the neurofilter has a nonlinear structure due to the sigmoid function. Numerical studies for the estimation of a second-order Butterworth process are performed by changing the structures of the neurofilter in order to evaluate the performance indices under changes of the output noises or disturbances. The results obtained from these studies verified the capabilities which are essentially necessary for on-line filtering of various stochastic signals.
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References
Sugisaka M, Sagara S, Ueno S (1991) On-line estimation and learning via neural network systems based on digital Chandrasekhar filter. Identification and parameter estimation, Pergamon, London, pp 533–537
Sugisaka M, Kumasiro M (1993) Filtering using artificial neural networks (in Japanese). The 12th SICE Kyushu Branch Conference, The Society of Instrument and Control Engineers, Kyushu Branch, Fukuoka, Japan, 1993, pp 93–94
Sugisaka M, Ueno S (1993) Chandrasekhar filtering using neural networks (in Japanese). Proceedings of the 15th Japanese Conference on Remote Sensing, Sanseidou Publishing Corp. Tokyo, Japan 1993, pp 227–228
Kailath T (1973) Some new algorithms for recursive estimation in constant linear systems. IEEE Trans Inform Theory IT-19: 750–760
Kailath T, Levy BC, Ljung L, Morf M (1978) Fast time-invariant implementations of Gaussian signal detectors. IEEE Trans Inform Theory IT-24: 469–477
Kailath T, Ljung L, Morf M (1982) Recursive input-output and state-space solutions for continuous-time linear estimation problems. IEEE Trans Automat Control AC-28: 897–906
Sugisaka M (1988) The design of Chandrasekhar-type filter via imbedding method. Rep Fac Eng Oita Univ 17: 19–22
Sugisaka M, Okada T (1994) Continuous-time Chandrasekhar smoother in detection and tracking. Appl Math Comput 69(1): 123–126
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Sugisaka, M. Neural filtering method for stationary signal processes. Artificial Life and Robotics 1, 165–168 (1997). https://doi.org/10.1007/BF02471134
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DOI: https://doi.org/10.1007/BF02471134