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17 results on '"Tanaka, Gouhei"'

2. An Echo State Network-Based Method for Identity Recognition with Continuous Blood Pressure Data

Author

Li, Ziqiang, Fujiwara, Kantaro, Tanaka, Gouhei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Iliadis, Lazaros, editor, Papaleonidas, Antonios, editor, Angelov, Plamen, editor, and Jayne, Chrisina, editor

Published
2023
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3. A Multi-Reservoir Echo State Network with Multiple-Size Input Time Slices for Nonlinear Time-Series Prediction

Author

Li, Ziqiang, Tanaka, Gouhei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mantoro, Teddy, editor, Lee, Minho, editor, Ayu, Media Anugerah, editor, Wong, Kok Wai, editor, and Hidayanto, Achmad Nizar, editor

Published
2021
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4. Two-Step FORCE Learning Algorithm for Fast Convergence in Reservoir Computing

Author

Tamura, Hiroto, Tanaka, Gouhei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Farkaš, Igor, editor, Masulli, Paolo, editor, and Wermter, Stefan, editor

Published
2020
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5. Dimensionality Reduction by Reservoir Computing and Its Application to IoT Edge Computing

Author

Yamane, Toshiyuki, Numata, Hidetoshi, Héroux, Jean Benoit, Kanazawa, Naoki, Takeda, Seiji, Tanaka, Gouhei, Nakane, Ryosho, Hirose, Akira, Nakano, Daiju, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Cheng, Long, editor, Leung, Andrew Chi Sing, editor, and Ozawa, Seiichi, editor

Published
2018
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6. Proposal of Carrier-Wave Reservoir Computing

Author

Hirose, Akira, Tanaka, Gouhei, Takeda, Seiji, Yamane, Toshiyuki, Numata, Hidetoshi, Kanazawa, Naoki, Heroux, Jean Benoit, Nakano, Daiju, Nakane, Ryosho, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Cheng, Long, editor, Leung, Andrew Chi Sing, editor, and Ozawa, Seiichi, editor

Published
2018
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7. Waveform Classification by Memristive Reservoir Computing

Author

Tanaka, Gouhei, Nakane, Ryosho, Yamane, Toshiyuki, Takeda, Seiji, Nakano, Daiju, Nakagawa, Shigeru, Hirose, Akira, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor

Published
2017
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8. Simulation Study of Physical Reservoir Computing by Nonlinear Deterministic Time Series Analysis

Author

Yamane, Toshiyuki, Takeda, Seiji, Nakano, Daiju, Tanaka, Gouhei, Nakane, Ryosho, Hirose, Akira, Nakagawa, Shigeru, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor

Published
2017
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9. Computational Performance of Echo State Networks with Dynamic Synapses

Author

Mori, Ryota, Tanaka, Gouhei, Nakane, Ryosho, Hirose, Akira, Aihara, Kazuyuki, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hirose, Akira, editor, Ozawa, Seiichi, editor, Doya, Kenji, editor, Ikeda, Kazushi, editor, Lee, Minho, editor, and Liu, Derong, editor

Published
2016
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10. Photonic Reservoir Computing Based on Laser Dynamics with External Feedback

Author

Takeda, Seiji, Nakano, Daiju, Yamane, Toshiyuki, Tanaka, Gouhei, Nakane, Ryosho, Hirose, Akira, Nakagawa, Shigeru, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hirose, Akira, editor, Ozawa, Seiichi, editor, Doya, Kenji, editor, Ikeda, Kazushi, editor, Lee, Minho, editor, and Liu, Derong, editor

Published
2016
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11. Dynamics of Reservoir Computing at the Edge of Stability

Author

Yamane, Toshiyuki, Takeda, Seiji, Nakano, Daiju, Tanaka, Gouhei, Nakane, Ryosho, Nakagawa, Shigeru, Hirose, Akira, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hirose, Akira, editor, Ozawa, Seiichi, editor, Doya, Kenji, editor, Ikeda, Kazushi, editor, Lee, Minho, editor, and Liu, Derong, editor

Published
2016
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12. Exploiting Heterogeneous Units for Reservoir Computing with Simple Architecture

Author

Tanaka, Gouhei, Nakane, Ryosho, Yamane, Toshiyuki, Nakano, Daiju, Takeda, Seiji, Nakagawa, Shigeru, Hirose, Akira, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hirose, Akira, editor, Ozawa, Seiichi, editor, Doya, Kenji, editor, Ikeda, Kazushi, editor, Lee, Minho, editor, and Liu, Derong, editor

Published
2016
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13. Wave-Based Reservoir Computing by Synchronization of Coupled Oscillators

Author

Yamane, Toshiyuki, Katayama, Yasunao, Nakane, Ryosho, Tanaka, Gouhei, Nakano, Daiju, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Arik, Sabri, editor, Huang, Tingwen, editor, Lai, Weng Kin, editor, and Liu, Qingshan, editor

Published
2015
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14. Multi-reservoir echo state networks with sequence resampling for nonlinear time-series prediction.

Author

Li, Ziqiang and Tanaka, Gouhei

Subjects
*COMPUTER systems, *OBJECT tracking (Computer vision), *FORECASTING, *TIME series analysis, *RESERVOIRS
Abstract

In this paper, we consider various schemes of sequence resampling in reservoir computing models for nonlinear time series prediction. These schemes can enrich the features used for training the readout part with batch learning and lead to better prediction performance. To implement these schemes, first, we introduce a modular approach for constructing multi-reservoir ESN models by assembling encoding and decoding modules. The encoding module is composed of a resampling unit, a group-wise reservoir unit, and a collection unit, for extracting various features from a sequence. The decoding module is a linear regressor which is trainable to produce desired outputs. Then, we propose three novel multi-reservoir ESN models, DeepESN with Every-layer Sequence Resampling (DeepESN-ESR), DeepESN with Last-layer Sequence Resampling (DeepESN-LSR), and GroupedESN with Input-layer Sequence Resampling (GroupedESN-ISR). These three models provide demonstrations of sequence resampling on multi-reservoir ESN models. Numerical results on five challenging nonlinear time-series prediction tasks show that the proposed models outperform some state-of-the-art multi-reservoir ESN models. An evaluation of computational time shows that our proposed three models require less computational cost for learning than many existing multi-reservoir ESN models in practice. Moreover, a comprehensive comparative analysis reveals that our proposed models are able to memorize longer temporal information and generate richer dynamics from the reservoir states than some existing models. The proposed schemes for extracting various features hidden in a sequence of reservoir states can be widely leveraged in other reservoir computing systems for improving their performance in nonlinear time series prediction. [ABSTRACT FROM AUTHOR]

Published
2022
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15. Transfer-RLS method and transfer-FORCE learning for simple and fast training of reservoir computing models.

Author

Tamura, Hiroto and Tanaka, Gouhei

Subjects
*LEAST squares, *ONLINE education, *RESERVOIRS, *MACHINE learning, *RECURRENT neural networks, *SUPERVISED learning
Abstract

Reservoir computing is a machine learning framework derived from a special type of recurrent neural network. Following recent advances in physical reservoir computing, some reservoir computing devices are thought to be promising as energy-efficient machine learning hardware for real-time information processing. To realize efficient online learning with low-power reservoir computing devices, it is beneficial to develop fast convergence learning methods with simpler operations. This study proposes a training method located in the middle between the recursive least squares (RLS) method and the least mean squares (LMS) method, which are standard online learning methods for reservoir computing models. The RLS method converges fast but requires updates of a huge matrix called a gain matrix, whereas the LMS method does not use a gain matrix but converges very slow. On the other hand, the proposed method called a transfer-RLS method does not require updates of the gain matrix in the main-training phase by updating that in advance (i.e., in a pre-training phase). As a result, the transfer-RLS method can work with simpler operations than the original RLS method without sacrificing much convergence speed. We numerically and analytically show that the transfer-RLS method converges much faster than the LMS method. Furthermore, we show that a modified version of the transfer-RLS method (called transfer-FORCE learning) can be applied to the first-order reduced and controlled error (FORCE) learning for a reservoir computing model with a closed-loop, which is challenging to train. [ABSTRACT FROM AUTHOR]

Published
2021
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16. Recent advances in physical reservoir computing: A review.

Author

Tanaka, Gouhei, Yamane, Toshiyuki, Héroux, Jean Benoit, Nakane, Ryosho, Kanazawa, Naoki, Takeda, Seiji, Numata, Hidetoshi, Nakano, Daiju, and Hirose, Akira

Subjects
*RECURRENT neural networks, *RESERVOIRS, *ARTIFICIAL neural networks, *NONLINEAR dynamical systems, *COMPUTER systems
Abstract

Reservoir computing is a computational framework suited for temporal/sequential data processing. It is derived from several recurrent neural network models, including echo state networks and liquid state machines. A reservoir computing system consists of a reservoir for mapping inputs into a high-dimensional space and a readout for pattern analysis from the high-dimensional states in the reservoir. The reservoir is fixed and only the readout is trained with a simple method such as linear regression and classification. Thus, the major advantage of reservoir computing compared to other recurrent neural networks is fast learning, resulting in low training cost. Another advantage is that the reservoir without adaptive updating is amenable to hardware implementation using a variety of physical systems, substrates, and devices. In fact, such physical reservoir computing has attracted increasing attention in diverse fields of research. The purpose of this review is to provide an overview of recent advances in physical reservoir computing by classifying them according to the type of the reservoir. We discuss the current issues and perspectives related to physical reservoir computing, in order to further expand its practical applications and develop next-generation machine learning systems. [ABSTRACT FROM AUTHOR]

Published
2019
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17. Multi-Reservoir Echo State Networks with Hodrick–Prescott Filter for nonlinear time-series prediction.

Author

Li, Ziqiang, Liu, Yun, and Tanaka, Gouhei

Subjects
TIME series analysis, PRINCIPAL components analysis, GREEDY algorithms, FORECASTING, KALMAN filtering, MACHINE learning
Abstract

The Echo State Network (ESN) is a representative model for reservoir computing, which is capable of high-speed model training for machine learning tasks with time series data. Extended models of the ESN, such as Multi-Reservoir ESNs (MRESNs), have been intensively studied for performance improvement in recent years. In this study, we propose a new model called an HP-MRESN by combining an MRESN with the Hodrick–Prescott (HP) filter for nonlinear time series prediction. The proposed HP-MRESN comprises three basic components: a time series decomposer, a reservoir state extractor, and an ensemble decoder. In the time series decomposer, we recursively leverage the HP filter to decompose original time-series data into multiple trend and cycle components. In the reservoir state extractor, each time series component is fed into a corresponding reservoir-state encoder for generating a reservoir state which is extracted as it is or through the principal component analysis. In the ensemble decoder, the states of multiple reservoirs are collected and processed to produce model outputs. Moreover, we propose a greedy algorithm to automatically find the best model architectures under designated hyperparameters for different prediction tasks. Experimental results on a total of 24 nonlinear time-series prediction tasks with 6 real-world datasets demonstrate that our proposed HP-MRESN not only can outperform some existing representative MRESN models and fully-trained RNN models but also can have relatively low training time. In addition, performance comparisons between the HP-MRESN and related MRESN models with other prepossessing methods show the benefit of time series decompositions using the HP filter. The codes of the proposed method are publicly available on https://github.com/Ziqiang-IRCN/HP-MRESN. • A new multi-reservoir echo state network model called HP-MRESN is proposed. • Time series decompositions using Hodrick–Prescott filters are employed. • The HP-MRESN yields high prediction performance with relatively low training cost. • A comprehensive comparison of various multi-reservoir ESN models is conducted. [ABSTRACT FROM AUTHOR]

Published
2023
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