Abstract
Many problems, arising in science and engineering, require the estimation of nonlinear, time-varying functions that map a set of input signals to a corresponding set of output signals. Some examples include: finding the relation between an input pressure signal and the movement of a pneumatic control valve; using past observations in a time series to predict future events; and using a group of biomedical signals to carry out diagnoses and prognoses. These problems can be reformulated in terms of a generic one of estimating the parameters of a suitable neural network on-line as the input-output data becomes available.
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© 2001 Springer Science+Business Media New York
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de Freitas, N., Andrieu, C., Højen-Sørensen, P., Niranjan, M., Gee, A. (2001). Sequential Monte Carlo Methods for Neural Networks. In: Doucet, A., de Freitas, N., Gordon, N. (eds) Sequential Monte Carlo Methods in Practice. Statistics for Engineering and Information Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-3437-9_17
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DOI: https://doi.org/10.1007/978-1-4757-3437-9_17
Publisher Name: Springer, New York, NY
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