Analyzing Divisia Rules Extracted from a Feedforward Neural Network

Vincent A. Schmidt, Jane M Binner

Research output: Book/ReportCommissioned report

Abstract

This paper introduces a mechanism for generating a series of rules that characterize the money-price relationship,
defined as the relationship between the rate of growth of the money supply and inflation. Division component data
is used to train a selection of candidate feedforward neural networks. The selected network is mined for rules,
expressed in human-readable and machine-executable form. The rule and network accuracy are compared, and
expert commentary is made on the readability and reliability of the extracted rule set. The ultimate goal of this
research is to produce rules that meaningfully and accurately describe inflation in terms of Divisia component
dataset.
Original languageEnglish
Number of pages8
Publication statusPublished - 1 Mar 2006

Bibliographical note

Approved for public release; distribution is unlimited. Cleared by AFRL/WS-06-0612 on 6 March 2006.

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