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Neural Computing and Applications

Erschienen in:

01.09.2011 | EANN 2009

Combining GRN modeling and demonstration-based programming for robot control

verfasst von: Wei-Po Lee, Tsung-Hsien Yang

Erschienen in: Neural Computing and Applications | Ausgabe 6/2011

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Abstract

In recent years, gene regulatory networks (GRNs) have been proposed to work as reliable and robust control mechanisms for robots. Because recurrent neural networks (RNNs) have the unique characteristic of presenting system dynamics over time, we thus adopt such kind of network structure and the principles of gene regulation to develop a biologically and computationally plausible GRN model for robot control. To simulate the regulatory effects and to make our model inferable from time-series data, we also implement an enhanced network-learning algorithm to derive network parameters efficiently. In addition, we present a procedure of programming-by-demonstration to collect behavior sequence data of the robot as expression profiles, and then employ our network-modeling framework to infer controllers. To verify the proposed approach, experiments have been conducted, and the results show that our regulatory model can be inferred for robot control successfully.

Vorheriger Artikel Nonmonotone Levenberg–Marquardt training of recurrent neural architectures for processing symbolic sequences

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Titel: Combining GRN modeling and demonstration-based programming for robot control
verfasst von: Wei-Po Lee
Tsung-Hsien Yang
Publikationsdatum: 01.09.2011
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 6/2011
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0496-z

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