A neural network model for nonlinear predictive coding in Fock space

Author

DeFigueiredo, Rui J P

Author_Institution

Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA

fYear

1993

fDate

3-6 May 1993

Firstpage

2164

Abstract

A generic nonlinear autoregressive (AR) model for a random time series is presented. The model is obtained by a nonlinear predictive coding (NLPC) approach which expresses the minimum mean square error estimate of the current value of the series as a Volterra series in terms of its immediate N preceding values. This Volterra series is assumed to belong to a generalized Fock Hilbert space F. In the second stage, which is parametric, the model parameters, which are coefficients of a linear combination of known nonlinear random functions of the data, are obtained by linear mean square estimation. The implementations of the model and of the estimator appear respectively as two layer recurrent and feedforward neural networks

Keywords

Volterra series; encoding; feedforward neural nets; prediction theory; recurrent neural nets; Fock space; Volterra series; feedforward neural networks; generic nonlinear autoregressive model; linear mean square estimation; minimum mean square error; model parameters; neural network model; nonlinear predictive coding; nonlinear random functions; random time series; recurrent neural networks; Equations; Hilbert space; Intelligent networks; Linear predictive coding; Mathematical model; Mathematics; Mean square error methods; Neural networks; Predictive coding; Predictive models;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1993., ISCAS '93, 1993 IEEE International Symposium on

Conference_Location

Chicago, IL

Print_ISBN

0-7803-1281-3

Type

conf

DOI

10.1109/ISCAS.1993.394187

Filename

394187