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Authors: Tarcisio R. Steinmetz ; Adelmo L. Cechin and Jose V. Canto dos Santos

Affiliation: PIPCA - UNISINOS, Brazil

ISBN: 978-989-8111-99-9

ISSN: 2184-2809

Keyword(s): Rule extraction from Artificial Neural Networks, Fuzzy Set Theory, Principal Components Analysis, Electric Load Demand Forecast.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Decision Support Systems ; Expert Systems ; Health Information Systems ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning in Control Applications ; Neural Networks Based Control Systems ; Symbolic Systems

Abstract: We present a methodology for the extraction of rules from Artificial Neural Networks (ANN) trained to forecast the electric load demand. The rules have the ability to express the knowledge regarding the behavior of load demand acquired by the network during training process. The rules are presented to the user in an easy to read format, such as IF premise THEN consequence. Where premise relates to the input data submitted to the network (mapped as fuzzy sets), and consequence appears as a linear equation describing the output to be presented by the network, should the premise part holds true. Experimentation demonstrates the method’s capacity for acquiring and presenting high quality rules from neural networks trained to forecast electric load demand for several amounts of time in the future.

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Paper citation in several formats:
R. Steinmetz T.; L. Cechin A.; V. Canto dos Santos J. and (2009). THE EXTRACTION OF KNOWLEDGE RULES FROM ARTIFICIAL NEURAL NETWORKS APPLIED IN THE ELECTRIC LOAD DEMAND FORECAST PROBLEM - How Artificial Neural Networks Retain Knowledge and Make Reliable Forecasts .In Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO, ISBN 978-989-8111-99-9, pages 195-200. DOI: 10.5220/0002198201950200

@conference{icinco09,
author={Tarcisio {R. Steinmetz} and Adelmo {L. Cechin} and Jose {V. Canto dos Santos}},
title={THE EXTRACTION OF KNOWLEDGE RULES FROM ARTIFICIAL NEURAL NETWORKS APPLIED IN THE ELECTRIC LOAD DEMAND FORECAST PROBLEM - How Artificial Neural Networks Retain Knowledge and Make Reliable Forecasts },
booktitle={Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,},
year={2009},
pages={195-200},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002198201950200},
isbn={978-989-8111-99-9},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,
TI - THE EXTRACTION OF KNOWLEDGE RULES FROM ARTIFICIAL NEURAL NETWORKS APPLIED IN THE ELECTRIC LOAD DEMAND FORECAST PROBLEM - How Artificial Neural Networks Retain Knowledge and Make Reliable Forecasts
SN - 978-989-8111-99-9
AU - R. Steinmetz, T.
AU - L. Cechin, A.
AU - V. Canto dos Santos, J.
PY - 2009
SP - 195
EP - 200
DO - 10.5220/0002198201950200

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