Learning dynamics from limited training data with multilayer perceptron initialized by weight prediction

Abstract

Real world data are often difficult to obtain. Logical machine learning methods can produce perfect explanations for dynamics of systems when the full state transitions can be observed, but such scenario is often impossible. Statistical machine learning methods also usually require a huge amount of data. In this work, we propose a method that predicts the initial weight of an MLP to learn a model that can predict future state of a delayed system even when only a limited amount of observation is provided. We also show the effectiveness of the method applied to systems with particularly a large number of variables.