Analyzing Divisia Rules Extracted from a Feedforward Neural Network


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.

Divisions: College of Business and Social Sciences > Aston Business School > Economics, Finance & Entrepreneurship
Additional Information: Approved for public release; distribution is unlimited. Cleared by AFRL/WS-06-0612 on 6 March 2006.
Last Modified: 27 Dec 2023 10:17
Date Deposited: 07 Feb 2019 14:29
PURE Output Type: Commissioned report
Published Date: 2006-03-01
Authors: Schmidt, Vincent A.
Binner, Jane M



Version: Published Version

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