Mechanism for Enhancement of Functionality in Deep Echo State Network by Optimizing Leaking Rate-Reference-Cited by-同舟云学术

Mechanism for Enhancement of Functionality in Deep Echo State Network by Optimizing Leaking Rate

Published:2023-09-21 Issue: Volume: Page:
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Container-title:2023 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)
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Author:

Inoue Shuichi¹,Nobukawa Sou¹,Nishimura Haruhiko²,Watanabe Eiji³,Isokawa Teijiro⁴

Affiliation:

1. Graduate School of Information and Computer Science, Chiba Institute of Technology,Narashino,JAPAN

2. Yamato University,Faculty of Informatics,Osaka,JAPAN

3. National Institute for Basic Biology,Okazaki,Japan

4. Graduate School of Engineering, University of Hyogo,Himeji,JAPAN

Funder

JSPS

KAKENHI

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10330803/10330926/10331600.pdf?arnumber=10331600

Reference27 articles.

1. Reservoir computing approaches to recurrent neural network training

2. Recent advances in physical reservoir computing: A review

3. Deep Reservoir Computing

4. The “echo state” approach to analysing and training recurrent neural networks-with an erratum note;Jaeger;Bonn, Germany: German National Research Center for Information Technology GMD Technical Report,2001

5. Pattern Classification by Spiking Neural Networks Combining Self-Organized and Reward-Related Spike-Timing-Dependent Plasticity

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1. Reservoir computing with a random memristor crossbar array;Nanotechnology;2024-07-25

2. Multi-scale dynamics by adjusting the leaking rate to enhance the performance of deep echo state networks;Frontiers in Artificial Intelligence;2024-07-16