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.
|Title of host publication||Artificial Neural Networks and Machine Learning – ICANN 2018|
|Subtitle of host publication||27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, Proceedings|
|Number of pages||14|
|Publication status||Published - 27 Sep 2018|
|Name||Lecture Notes in Computer Science|