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Authors: Weiwei Yu 1 ; Kurosh Madani 2 and Christophe Sabourin 2

Affiliations: 1 Northwestern Polytechnical University, China ; 2 UPEC University and Senart-Fontainebleau Institute of Technology, France

ISBN: 978-989-8425-84-3

Keyword(s): CMAC neural network, Structural parameters, Q-learning, Structure optimization.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Higher Level Artificial Neural Network Based Intelligent Systems ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Theory and Methods

Abstract: Comparing with other neural networks based models, CMAC is successfully applied on many nonlinear control systems because of its computational speed and learning ability. However, for high-dimensional input cases in real application, we often have to make our choice between learning accuracy and memory size. This paper discusses how both the number of layer and step quantization influence the approximation quality of CMAC. By experimental enquiry, it is shown that it is possible to decrease the memory size without losing the approximation quality by selecting the adaptive structural parameters. Based on Q-learning approach, the CMAC structural parameters can be optimized automatically without increasing the complexity of its structure. The choice of this optimized CMAC structure can achieve a tradeoff between the learning accuracy and finite memory size. At last, the application of this Q-learning based CMAC structure optimization approach on the joint angle tracking problem for biped robot is presented. (More)

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Paper citation in several formats:
Yu, W.; Madani, K. and Sabourin, C. (2011). CMAC STRUCTURE OPTIMIZATION WITH Q-LEARNING APPROACH AND ITS APPLICATION.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 283-288. DOI: 10.5220/0003694102830288

@conference{ncta11,
author={Weiwei Yu. and Kurosh Madani. and Christophe Sabourin.},
title={CMAC STRUCTURE OPTIMIZATION WITH Q-LEARNING APPROACH AND ITS APPLICATION},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={283-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003694102830288},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - CMAC STRUCTURE OPTIMIZATION WITH Q-LEARNING APPROACH AND ITS APPLICATION
SN - 978-989-8425-84-3
AU - Yu, W.
AU - Madani, K.
AU - Sabourin, C.
PY - 2011
SP - 283
EP - 288
DO - 10.5220/0003694102830288

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