Title :
Optimality of transformations for parameter estimation
Author :
Apollo, S.J. ; Manry, M.T. ; Allen, L.S. ; Lyle, W.D.
Author_Institution :
Gen. Dynamics, Fort Worth, TX, USA
Abstract :
Neural-network-based minimum-mean-square (MMS), maximum-a-posteriori (MAP), and maximum-likelihood (ML) parameter estimation are considered. The multilayer perceptron (MLP) is shown to approximate the minimum mean square estimator. Linear transforms are used to compress data for the purpose of efficient parameter estimation. Raw and transform domain lower bounds are developed and used as an optimality criterion in a procedure defined to compare various transformations
Keywords :
discrete cosine transforms; feedforward neural nets; maximum likelihood estimation; parameter estimation; transforms; Legendre transform; MAP estimation; MLE; Walsh-Hadamard transform; discrete cosine transform; linear transforms; maximum likelihood estimation; maximum-a-posteriori estimation; minimum mean square estimator; multilayer perceptron; neural network; optimality criterion; parameter estimation; transform domain lower bounds; transformations; Equations; Maximum likelihood estimation; Mean square error methods; Multilayer perceptrons; Neural networks; Parameter estimation; Research and development; Statistics; Topology; Training data;
Conference_Titel :
Signals, Systems and Computers, 1992. 1992 Conference Record of The Twenty-Sixth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-3160-0
DOI :
10.1109/ACSSC.1992.269187
