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Mass-Spring Damper Array as a Mechanical Medium for Computation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2018
Subtitle of host publication27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, Proceedings
Publisher or commissioning bodySpringer, Cham
Number of pages14
ISBN (Electronic)9783030014330
ISBN (Print)9783030014230
DateAccepted/In press - 10 Jul 2018
DatePublished (current) - 27 Sep 2018

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Recently, it has been reported that the dynamics of mechanical structures can be used as a computational resource—also referred to as morphological computation. In particular soft materials have been shown to have the potential to be used for time series forecasting. Although most soft materials can be modeled by mass-spring systems, a limited number of researches has been performed on the computational capabilities of such systems. In this paper, we propose an array of masses linked in a gridlike structure by spring-damper connections to investigate systematically the influence of structural (size) and dynamic (stiffness, damping) parameters on the computational capabilities for time series forecasting. In addition, such a structure gives us a good approximation of two-dimensional elastic media, e.g., a rubber sheet, and therefore a direct pathway to potentially implement results in a real system. In particular, we compared the mass-spring array to echo state networks, which are standard machine learning techniques for this kind of problems and are also closely related to the underlying theoretical models applied when exploiting mechanical structures for computation. Our results suggest a clear connection of morphological features to computational capabilities.

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