A NEURAL-NET MODEL FOR PREDICTION

• 成果类型: Article
• 署名作者: POLI, I; JONES, RD
• 署名单位: United States Department of Energy (DOE); Los Alamos National Laboratory
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2291206
• 发表日期: 1994
• 页码: 117-121
• 关键词: chaotic time-series; Kalman filter

摘要

In this article we introduce a neural net designed for nonlinear statistical prediction. The net is based on a stochastic model featuring a multilayer feedforward architecture with random connections between units and noisy response functions. A Bayesian inferential procedure for this model, based on the Kalman filter, is derived. The resulting learning algorithm generalizes the so-called one-dimensional Newton method, an updating algorithm currently popular in the neural net literature. A numerical study concerning the prediction of a noisy chaotic time series is presented, and the greater predictive accuracy of the new algorithm with respect to the Newton algorithm is exhibited.