Analyzing Divisia Rules Extracted from a Feedforward Neural Network

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 Division component dataset.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2006
Accession Number
ADA457596

Entities

People

  • Jane M. Binner
  • Vincent A. Schmidt

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Coding
  • Computing System Architectures
  • Costs
  • Data Mining
  • Mathematical Models
  • Military Research
  • Models
  • Network Architecture
  • Neural Networks
  • Training

Fields of Study

  • Computer science
  • Engineering

Readers

  • Computational Modeling and Simulation
  • Government and Public Administration Law.
  • Radio communications and signal processing.

Technology Areas

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