Development of Cellular Neural Networks with Complex Network Structures and their Applications to Information Processing
| Project/Area Number |
16K06357
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Communication/Network engineering
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| Research Institution | The University of Tokushima |
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Principal Investigator |
NISHIO Yoshifumi 徳島大学, 大学院社会産業理工学研究部(理工学域), 教授 (80253227)
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| Co-Investigator(Kenkyū-buntansha) |
上手 洋子 徳島大学, 大学院社会産業理工学研究部(理工学域), 准教授 (80582642)
細川 康輝 四国大学, 経営情報学部, 准教授 (20341266)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | セルラニューラルネットワーク / 複雑ネットワーク / 非線形回路 / カオス / セルラーニューラルネットワーク / 複雑系ネットワーク / 脳情報処理 |
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| Outline of Final Research Achievements |
In this study, new complex cellular neural networks including features of complex networks are developed. The proposed networks, whose circuit structure changes according to the input images or which includes delay components partially, could realize the image processing which was difficult by the conventional networks. They were confirmed by circuit experiments, numerical simulations by computers, and theoretical analysis. Further, propagation phenomnea of infromation over the network whose nodes are chaotic circuits were investigated, and the effect of the topology and the connection strengths of the networks on the whole network were clarified.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究で対象とした高機能情報処理システムは、画像処理・パターン認識・データ分類・特徴抽出・データ予測・暗号化・符号化など、工学の様々な分野で必要とされる基盤技術であり、その適用範囲は非常に広いため社会的な波及効果も大きい。
さらに、優れた応用を示すことは、複雑系ネットワークへの一般の関心を集め、複雑系科学、神経科学、非線形回路工学の研究分野の発展にも貢献できる。
複雑系ネットワークは、脳の情報処理モデルとしても期待されているため、本研究の成果は、人間の脳による情報処理モデルとしても期待できる。
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Report
(4 results)
Research Products
(52 results)
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[Journal Article] Denoising Auto Encoder with Logistic Map2017
Author(s)
Ryuta YOSHIMURA, Shinsaburo KITTAKA, Yoko UWATE and Yoshifumi NISHIO
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Journal Title
Proceedings of RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing
Volume: n/a
Pages: 57-60
Related Report
Peer Reviewed / Open Access / Acknowledgement Compliant
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