IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2020-06-18 10:25
On numerical approximated solutions of an ordinary differential\ equation using a LSTM neural network
Kazuya Ozawa, Kaito Isogai, Hideaki Okazaki (SIT) CAS2020-2 VLD2020-2 SIP2020-18 MSS2020-2
Abstract (in Japanese) (See Japanese page) 
(in English) Recurrent neural networks (RNNs) were demonstrated to provide good accuracy when modeling nonlinear circuits. However, since the training
algorithm of RNN needs the backpropogation through time(BPTT), this has a Vanishing gradient problem. Long-Short Term Memory (LSTM) which is a type of RNNs uses several gated units to avoid this probem. In this paper, LSTM is applied to estimate perodic behavior of Colpitts oscillator. The numerical approximated solutions of Colpitts oscillator ordinary differential equation using the LSTM neural network are discussed.
Keyword (in Japanese) (See Japanese page) 
(in English) LSTM Neural Network / Colpitts Oscillator / Differential equations / Approximation / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 65, CAS2020-2, pp. 7-9, June 2020.
Paper # CAS2020-2 
Date of Issue 2020-06-11 (CAS, VLD, SIP, MSS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF CAS2020-2 VLD2020-2 SIP2020-18 MSS2020-2

Conference Information
Committee MSS CAS SIP VLD  
Conference Date 2020-06-18 - 2020-06-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To CAS 
Conference Code 2020-06-MSS-CAS-SIP-VLD 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) On numerical approximated solutions of an ordinary differential\ equation using a LSTM neural network 
Sub Title (in English)  
Keyword(1) LSTM Neural Network  
Keyword(2) Colpitts Oscillator  
Keyword(3) Differential equations  
Keyword(4) Approximation  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Kazuya Ozawa  
1st Author's Affiliation Shonan Institute of Technology (SIT)
2nd Author's Name Kaito Isogai  
2nd Author's Affiliation Shonan Institute of Technology (SIT)
3rd Author's Name Hideaki Okazaki  
3rd Author's Affiliation Shonan Institute of Technology (SIT)
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Speaker Author-1 
Date Time 2020-06-18 10:25:00 
Presentation Time 25 minutes 
Registration for CAS 
Paper # CAS2020-2, VLD2020-2, SIP2020-18, MSS2020-2 
Volume (vol) vol.120 
Number (no) no.65(CAS), no.66(VLD), no.67(SIP), no.68(MSS) 
Page pp.7-9 
#Pages
Date of Issue 2020-06-11 (CAS, VLD, SIP, MSS) 


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan