Reservoir-based 1D convolution: low-training-cost AI

IF 0.4 4区计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Ieice Transactions on Fundamentals of Electronics Communications and Computer Sciences Pub Date : 2023-01-01 DOI:10.1587/transfun.2023eal2050

Yuichiro TANAKA, Hakaru TAMUKOH

引用次数: 0

Abstract

In this study, we introduce a reservoir-based one-dimensional (1D) convolutional neural network that processes time-series data at a low computational cost, and investigate its performance and training time. Experimental results show that the proposed network consumes lower training computational costs and that it outperforms the conventional reservoir computing in a sound-classification task.

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基于油藏的1D卷积:低训练成本的人工智能

在本研究中，我们引入了一种基于水库的一维卷积神经网络，该网络以较低的计算成本处理时间序列数据，并研究了其性能和训练时间。实验结果表明，该网络的训练计算成本较低，在声音分类任务中优于传统的储层计算。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ieice Transactions on Fundamentals of Electronics Communications and Computer Sciences 工程技术-工程：电子与电气

CiteScore

1.10

自引率

20.00%

发文量

137

审稿时长

3.9 months

期刊介绍： Includes reports on research, developments, and examinations performed by the Society''s members for the specific fields shown in the category list such as detailed below, the contents of which may advance the development of science and industry: (1) Reports on new theories, experiments with new contents, or extensions of and supplements to conventional theories and experiments. (2) Reports on development of measurement technology and various applied technologies. (3) Reports on the planning, design, manufacture, testing, or operation of facilities, machinery, parts, materials, etc. (4) Presentation of new methods, suggestion of new angles, ideas, systematization, software, or any new facts regarding the above.