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Neural Network-based minimum mean square (MMS), maximum a posteriori (MAP), and maximum likelihood (ML) parameter estimation are considered here. Ihe multilayer perceptron (MLP) is shown to approximate the minimum mean square estimator. Linear transfonns are used to compress data for the purpose of eficient parameter estimation. Raw and transform domain lower b o u h are developed and used as an optimality criteria in a procedure defined to compare various transfomations.

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