A Connectionist Approach to Producing Rules Describing Monthly UK Divisia Data

Abstract

This paper demonstrates a mechanism whereby rules can be extracted from a feedforward neural network trained to characterize the money-price relationship, defined as the relationship between the rate of growth of the money supply and inflation. Monthly Divisia component data is encoded and used to train a group of candidate connectionist architectures. One candidate is selected for rule extraction, using a custom decompositional extraction algorithm that generates rules in human-readable and machine-executable form. Rule and network accuracy are compared, and comments are made on the relationships expressed within the discovered rules. The types of discovered relationships could be used to guide monetary policy decisions.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2008
Accession Number
ADA514706

Entities

People

  • Jane M. Binner
  • Vincent A. Schmidt

Organizations

  • Aston University

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Computing System Architectures
  • Data Mining
  • Demographic Cohorts
  • Extraction
  • Government Procurement
  • Governments
  • Information Science
  • Military Research
  • Monetary Policy
  • Network Architecture
  • Neural Networks

Readers

  • Artificial Intelligence
  • Economics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks