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KAKEN_15K06081seika.pdf
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Title |
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再生核適応フィルタの解析と高性能アルゴリズム開発
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Kana |
サイセイカク テキオウ フィルタ ノ カイセキ ト コウセイノウ アルゴリズム カイハツ
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Saiseikaku tekiō firuta no kaiseki to kōseinō arugorizumu kaihatsu
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Analysis and developments of kernel adaptive filtering algorithm
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湯川, 正裕
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ユカワ, マサヒロ
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Yukawa, Masahiro
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慶應義塾大学・理工学部 (矢上) ・准教授
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Research team head
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科研費研究者番号 : 60462743
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2019
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科学研究費補助金研究成果報告書
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2018
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信号処理・データサイエンスにおいて、非線形性を考慮すべき状況が広く見られる。例えば、低品質スピーカーの入出力特性、光通信路の伝搬特性、時系列データにおける過去と未来のデータ間の関係性など、枚挙に暇がない。ガウス過程のオンライン版として位置付けられるカーネル適応フィルタは、推定精度・計算量・大域的最適性(凸性)の点で優れている。
本研究では、カーネル設計が容易で多重スケール性に対応できるという特長を持つ多カーネル適応フィルタの解析と高性能化に取り組んだ。高速な収束を達成するアルゴリズム開発、高速性のメカニズム解明、再生核が存在しない空間への拡張、分散型への拡張に関する成果を得た。
Nonlinearity is encountered in many situations in signal processing and data science, such as in characteristics of low-cost speaker, channel of optical communication, relationship between the past and future measurements of time-series data, to name a few. Kernel adaptive filtering has good tradeoffs among estimation accuracy, complexity, and global optimality (convexity).
In the current research project, analysis and performance improvements of multikernel adaptive filtering have been addressed. The multikernel adaptive filtering is insensitive to the design of kernels and is able to capture the multiscaleness of data. The outcomes of the project include the developments of fast converging algorithms with understanding of its mechanism, an extension to Hilbert spaces that have no reproducing kernels, and an extension to distributed settings with sensor networks envisioned.
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研究種目 : 基盤研究 (C) (一般)
研究期間 : 2015~2018
課題番号 : 15K06081
研究分野 : 信号処理
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