Exploiting the Dynamics of Soft Materials for Machine Learning.

ABSTRACT: Soft materials are increasingly utilized for various purposes in many engineering applications. These materials have been shown to perform a number of functions that were previously difficult to implement using rigid materials. Here, we argue that the diverse dynamics generated by actuating soft materials can be effectively used for machine learning purposes. This is demonstrated using a soft silicone arm through a technique of multiplexing, which enables the rich transient dynamics of the soft materials to be fully exploited as a computational resource. The computational performance of the soft silicone arm is examined through two standard benchmark tasks. Results show that the soft arm compares well to or even outperforms conventional machine learning techniques under multiple conditions

SUBMITTER: Nakajima K 

PROVIDER: S-EPMC5995269 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature