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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP | 2022-11-25 11:10 |
Shiga | (Primary: On-site, Secondary: Online) |
[Invited Talk]
Chaotic time series and Ueda's theory of chaos Takaya Miyano (Ritsumeikan Univ.)  NLP2022-72 |
In terms of Ueda’s theory of chaos, i.e., the concept of randomly transitional oscillations, we discuss the implications... [more] | NLP2022-72 pp.71-72 |
| NLP | 2022-08-02 09:25 |
Online | Online | Performance Evaluation of Time Series Forecasting with Chaotic Neural Network Reservoir using ReLU Derived Functions Tatsuya Saito, Misa Fujita (Chukyo Univ.) NLP2022-27 |
Reservoir computing has been attracting attention in recent years. It can learn time-series data at high speed. Th... [more] |
NLP2022-27 pp.7-10 |
| NLP | 2021-12-18 14:50 |
Oita | J:COM Horuto Hall OITA | Experiment of time series signal classification task using 3D cyclic chaotic neural network reservoir Takemori Orima, Yoshihiko Horio (Tohoku Univ.) NLP2021-65 |
The chaotic neural network reservoir composed of chaotic neurons can perform time-series signal processing with a smalle... [more] | NLP2021-65 pp.100-103 |
| NLP | 2021-12-18 15:40 |
Oita | J:COM Horuto Hall OITA | Performance evaluation on timeseries prediction of multi-layer simple cycle reservoir computing Kentaro Imai, Masaharu Adachi (Tokyo Denki Univ.) NLP2021-67 |
The purpose of this study is to combine Deep Echo State Network with other models. In this study, we propose and impleme... [more] | NLP2021-67 pp.110-113 |
| RCC, MICT | 2019-05-29 15:30 |
Tokyo | TOKYO BIG SIGHT | [Poster Presentation]
Vibration time series analysis using persistent homology Koki Terauchi (Osaka City Univ.), Kazunori Hayashi (Osaka City Univ./RIKEN), Jing Di, Yoji Okada (SEI) RCC2019-10 MICT2019-10 |
In this research, in order to diagnose a failure of consumable parts of a rotating machine by using vibration time serie... [more] | RCC2019-10 MICT2019-10 pp.47-52 |
| NLP, NC (Joint) |
2019-01-23 11:00 |
Hokkaido | The Centennial Hall, Hokkaido Univ. | Performance Evaluation of Chaotic Random Numbers Using Integer Logistic Map by NIST Test Shiki Kanamaru (TUS), Yutaka Shimada (Saitama Univ.), Kantaro Fujiwara (UT), Tohru Ikeguchi (TUS) NLP2018-100 |
In this report, we generated pseudorandom numbers using chaotic dynamics and investigated its performance as pseudorando... [more] | NLP2018-100 pp.23-28 |
| NLP, CCS | 2018-06-08 09:45 |
Kyoto | Kyoto Terrsa | Application of tensor decomposition to chaotic itinerancy time series Takahiro Arai, Toshio Aoyagi (Kyoto Univ.) NLP2018-29 CCS2018-2 |
Tensor decomposition is a typical method for analyzing resting-state BOLD signals. This method can decompose the observe... [more] | NLP2018-29 CCS2018-2 pp.7-12 |
| NLP | 2017-11-05 17:25 |
Miyagi | Research Institute of Electrical Communication Tohoku University | Reconstructing Bifurcation Diagrams of Chaotic neuron Using Extreme Learning Machine Yoshitaka Itoh, Masaharu Adachi (Tokyo Denki Univ.) NLP2017-74 |
In recent year, an extreme learning machine which is simple structure have been applied to various problems. As one of t... [more] | NLP2017-74 pp.53-58 |
| NLP, CCS | 2015-06-12 14:00 |
Tokyo | Waseda Univerisity | Binary chaotic cryptography using augmented Lorenz equations Kenichiro Cho, Takaya Miyano (Rits Univ) NLP2015-62 CCS2015-24 |
Augmented Lorenz equations are expressed as a star network of N Lorenz subsystems sharing the scalar variable X as the c... [more] | NLP2015-62 CCS2015-24 pp.135-137 |
| SIS | 2013-06-14 10:50 |
Kagoshima | Houzan Hall (Kagoshima) | Modeling for Events characterizing Simultaneous Change in Time Series Parameters by using Coupled Pricing and Parameter Estimation based on Bayesian Method Shozo Tokinaga (Kyushu Univ.), Yoshikazu Ikeda (Kitakyushu Univ.) SIS2013-11 |
This report deals with modeling for events characterizing simultaneous change in time series parameters by using co... [more] |
SIS2013-11 pp.53-58 |
| NLP | 2009-11-11 11:10 |
Kagoshima | Chaotic Time Series Prediction by Combining Echo-State Networks and Radial Basis Function Networks Yoshitaka Itoh, Masaharu Adachi (Tokyo Denki Univ.) NLP2009-86 |
In this report, we describe a chaotic time series prediction method by a network which combines echo state networks (ES... [more] |
NLP2009-86 pp.27-30 |
|
| NC | 2009-01-20 10:00 |
Hokkaido | Hokkaido Univ. | [Invited Talk]
Chaotic itinerancy in the hippocampal CA3 and contractive affine transformations in CA1 provide a dynamical interpretation of complex memory Ichiro Tsuda (Hokkaido Univ.) NC2008-96 |
Since David Marr's pioneering paper entitled “Simple memory: A model for the archecortex” published in 1969, the functio... [more] | NC2008-96 pp.85-86 |
| NLP | 2008-12-10 16:30 |
Ishikawa | Prediction of chaotic time series by neural networks with an improved particle swarm optimization Hironori Nakamura, Masaharu Adachi (Denki Univ.) NLP2008-91 |
In this report, we describe prediction of chaotic time series. In concrete, we use neural networks as a prediction sys... [more] |
NLP2008-91 pp.113-118 |
|
| NLP | 2008-03-27 15:00 |
Hyogo | Analysis method of chaotic time series using measures of networks. Yutaka Shimada, Tohru Ikeguchi (Saitama Univ.) NLP2007-161 |
Complex phenomena are ubiquitous in the real world, for example, fluctuation of financial indices in a stock market, po... [more] |
NLP2007-161 pp.43-48 |
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