2001 | OriginalPaper | Buchkapitel
Sequential Monte Carlo Methods for Neural Networks
verfasst von : N. de Freitas, C. Andrieu, P. Højen-Sørensen, M. Niranjan, A. Gee
Erschienen in: Sequential Monte Carlo Methods in Practice
Verlag: Springer New York
Enthalten in: Professional Book Archive
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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.