| 講演名 | 2018-12-22 Linear processing type optimum future prediction of signals applying Kida's optimum signal-approximation to multi-dimensional signals that are made by arranging known region restriction data in a row Takuro Kida(東工大), Yuichi Kida(OHU Univ.), |
|---|---|
| PDFダウンロードページ |  PDFダウンロードページへ |
| 抄録(和) | |
| 抄録(英) | With respect to a matrix-filterbank that the matrix analysis-filterbank ${bf H}$ and the matrix sampling-filterbank ${bf S}$ are given, it is accomplished to present the optimum matrix synthesis-filterbank ${bf Z}$ that minimizes all the worst-case measures of matrix-error-signals ${bf E}(omega)={bf F}(omega)-{bf Y}(omega)$ between the input matrix-signals ${bf F}(omega)$ and the output matrix-signals ${bf Y(omega)}$ of the matrix-filterbank, at the same time. In this analysis, we assume that a set of the one-dimensional scanned input matrix-signals ${bf f}(t)={bf f}({bf x}(t))$, $({bf x}(t)=(x_0(t), x_1(t), ldots, x_{N-1}(t))$ of the multi-dimensional input matrix-images ${bf f}({bf x})$, $({bf x}=(x_0, x_1, ldots, x_{N-1}))$, is given. We assume that ${bf f}(t)$ is band-limited with an arbitrary given band-width and is allowed to include that ${bf f}(t)$ has uniformly or non-uniformly arranged sample-values. %\ %hspace*{3mm}Based on the concept of pseudo-inverse-matrix, we prove that the optimum synthesis-filterbank ${bf Z}$ is equal to the synthesis-filterbank that minimizes the upper-limit of a given matrix-norm of the error ${bf E}(omega)={bf F}(omega)-{bf Y}(omega)$ among all the input matrix-signals ${bf F}(omega)$ contained in the set of ${bf F}(omega)$. As the consequence of this fact, it is shown that there exists a linear calculation method which gives the optimum synthesis-matrix ${bf Z}$ by solving a set of linear equations. This result shows that, among all AI approximate estimation systems including well-known deep learning systems, there exists an optimum linear approximation system based on the set of the one-dimensional scanned data of the given multi-dimensional knowledge-data that is considered as the scanned input matrix-images ${bf f}({bf x}(t))$. In the final part of this paper, we show that there exists the explicit relation between the presented optimum approximation and the artificial intelligent system based on the given past knowledge data. |
| キーワード(和) | |
| キーワード(英) | signal approximationpseudo inverse matrixartificial intelligence |
| 資料番号 | DE2018-29 |
| 発行日 | 2018-12-14 (DE) |
| 研究会情報 | |
| 研究会 | DE / IPSJ-DBS |
|---|---|
| 開催期間 | 2018/12/21(から2日開催) |
| 開催地（和） | 国立情報学研究所(NII) |
| 開催地（英） | National Institute of Informatics |
| テーマ（和） | データ工学・データベースシステムとエンターテイメントおよび一般 |
| テーマ（英） | |
| 委員長氏名（和） | 灘本 明代(甲南大) / 吉川 正俊(京大) |
| 委員長氏名（英） | Akiyo Nadamoto(Konan Univ.) / 吉川 正俊(京大) |
| 副委員長氏名（和） | 宮崎 純(東工大) / 大塚 真吾(神奈川工科大) |
| 副委員長氏名（英） | Jun Miyazaki(Tokyo Inst. of Tech.) / Shingo Otsuka(Kanagawa Inst. of Tech.) |
| 幹事氏名（和） | 大島 裕明(兵庫県立大) / 上田 真由美(流通科学大) / 天笠 俊之(筑波大) |
| 幹事氏名（英） | Hiroaki Ohshima(Univ. of Hyogo) / Mayuki Ueda(Univ. of Marketing and Distrbution Science) / 天笠 俊之(筑波大) |
| 幹事補佐氏名（和） | 合田 和生(東大) / 塩川 浩昭(筑波大) |
| 幹事補佐氏名（英） | Kazuo Goda(Univ. of Tokyo) / Hiroaki Shiokawa(Tsukuba Univ.) |
| 講演論文情報詳細 | |
| 申込み研究会 | Technical Committee on Data Engineering / Special Interest Group on Database System |
|---|---|
| 本文の言語 | ENG |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Linear processing type optimum future prediction of signals applying Kida's optimum signal-approximation to multi-dimensional signals that are made by arranging known region restriction data in a row |
| サブタイトル（和） | |
| キーワード(1)（和/英） | / signal approximationpseudo inverse matrixartificial intelligence |
| 第 1 著者 氏名（和/英） | Takuro Kida / Takuro Kida |
| 第 1 著者 所属（和/英） | Professor Emeritus, Tokyo Institute of Technology(略称：東工大) Professor Emeritus, Tokyo Institute of Technology(略称：Tokyo Inst. Tech.) |
| 第 2 著者 氏名（和/英） | Yuichi Kida / Yuichi Kida |
| 第 2 著者 所属（和/英） | The School of Pharmaceutical Sciences, Ohu University(略称：OHU Univ.) The School of Pharmaceutical Sciences, Ohu University(略称：OHU Univ.) |
| 発表年月日 | 2018-12-22 |
| 資料番号 | DE2018-29 |
| 巻番号（vol） | vol.118 |
| 号番号（no） | DE-377 |
| ページ範囲 | pp.65-70(DE), |
| ページ数 | 6 |
| 発行日 | 2018-12-14 (DE) |