2019年7月14~19日にハンガリーのブダペストで開催された国際会議 International Joint Conference on Neural Networks (IJCNN2019)において、本学複雑系知能学科の香取勇一准教授らの論文”Reservoir Computing Based on Dynamics of Pseudo-Billiard System in Hypercube(超立方体上の疑似ビリヤード系ダイナミクスに基づくリザバー計算)”がBest Paper Award(最優秀論文賞)を受賞しました。採択された800以上の論文から最も優れた論文として選ばれたものです。



Reservoir Computing Based on Dynamics of Pseudo-Billiard System in Hypercube
Yuichi Katori 1, Hakaru Tamukoh 2, Takashi Morie 2

1: Future University Hakodate, 2: Kyushu Institute of Technology Abstract
—Reservoir computing (RC) is a framework for constructing recurrent neural networks with simple training rule and sparsely and randomly connected nonlinear units. The network (called reservoir) generates complex motion that can be used for many tasks including time series generation and prediction. We construct a reservoir based on the dynamics of the pseudo-billiard system that produce complex motion in a high-dimensional hypercube. In particular, we use the chaotic Boltzmann machine (CBM) whose units exhibit chaotic behavior in the hypercube. The units interact with each other in a time-domain manner through its binary state, and thus an efficient hardware implementation of the system is expected. In order to utilize the CBM as the reservoir, it is necessary to control its chaotic behavior for ensuring the echo state property of RC and establish encoding and decoding for input and output signal. For this purpose, we introduce a reference clock and analyze effects and properties of the reference input. We evaluate the proposed model on the time series generation tasks and show that the model works properly on a broad range of parameter values. Our approach presents a novel mechanism for time-domain information processing and a fundamental technology for a brain like artificial intelligence system.