Abstract
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 grid-like 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.
Supported by JST, PRESTO Grant Number JPMJPR15E7 and JPMJPR16EC, Japan and by the Leverhulme Trust Research Project Grant RPG-2016-345.
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Notes
- 1.
The spectral radius of the matrix is the largest absolute value of the eigenvalues of the matrix. The performance of ESNs strongly depends on if the network has the so-called echo state property, and it is known that the small spectral radius indicates this property. See [7] for detail.
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Yamanaka, Y., Yaguchi, T., Nakajima, K., Hauser, H. (2018). Mass-Spring Damper Array as a Mechanical Medium for Computation. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_76
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