Development of fast calculation-type fuzzy inference models considering big data

• Project/Area Number: 15K16065
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Soft computing
• Research Institution: Osaka University
• Principal Investigator: Seki Hirosato 大阪大学, 基礎工学研究科, 助教 (10583693)
• Project Period (FY): 2015-04-01 – 2018-03-31
• Project Status: Completed (Fiscal Year 2017)
• Budget Amount: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000); Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000); Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000); Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
• Keywords: ソフトコンピューティング / ファジィ推論 / 単一入力型ファジィ推論モデル / ビッグデータ解析 / 等価性 / 単調性 / 不精密ルール / 高速演算 / ビッグデータ / 計算知能 / ファジィシステム

Outline of Final Research Achievements

The single input type fuzzy inference models that unifies the inference outputs from fuzzy rule modules of one input type IF-THEN form can sharply reduce the number of fuzzy rules compared with conventional fuzzy inference models. However, since the number of rules of the single input type fuzzy inference models were limited as compared to the conventional fuzzy inference models, the inference results were simple in general. Therefore, several extended single input type fuzzy inference models are proposed in this study, and their properties are clarified from theoretical point of view. Moreover, their learning algorithms are proposed, and applied them to a medical diagnosis as a real system. Finally, the fuzzy inference models with imprecise rules and OR-type fuzzy rules are proposed. The inference results by these proposed models can be obtained by the fast calculation method. From the above results, it turns out that the proposed models are useful for big data.

Research Products

• [Journal Article] Optimization of Constrained SIRMs Connected Type Fuzzy Inference Model Using Two-Phase Simplex Method (2018)
  • Author(s): Takeshi Nagata, Hirosato Seki, Hiroaki Ishii
  • Journal Title: Journal of Advanced Computational Intelligence and Intelligent Informatics Volume: 22 Issue: 2 Pages: 172-175
  • DOI: 10.20965/jaciii.2018.p0172
  • NAID: 130007494905
  • ISSN: 1343-0130, 1883-8014
  • Year and Date: 2018-03-20
  • Peer Reviewed
• [Journal Article] On the Learning Method, Properties of the Extended Functional-Type SIRMs Connected Fuzzy Inference Model and Their Application to a Medical Diagnosis System (2018)
  • Author(s): Diederik van Krieken, Hirosato Seki, Masahiro Inuiguchi
  • Journal Title: Journal of Advanced Computational Intelligence and Intelligent Informatics Volume: 22 Issue: 2 Pages: 176-183
  • DOI: 10.20965/jaciii.2018.p0176
  • NAID: 130007491616
  • ISSN: 1343-0130, 1883-8014
  • Year and Date: 2018-03-20
  • Peer Reviewed / Int'l Joint Research
• [Journal Article] ファジィとCI (2015)
  • Author(s): 関宏理
  • Journal Title: 計測と制御 Volume: 54 Pages: 573-578
  • NAID: 130005094576
• [Presentation] Application to a medical diagnostic system by a fuzzy inference model with imprecise rules (2018)
  • Author(s): Ryo Yamashita, Hirosato Seki, and Masahiro Inuiguchi
  • Organizer: 2018 International Conference on Electronics, Information, and Communication
  • Int'l Joint Research
• [Presentation] 曖昧性を考慮した意思決定 (2017)
  • Author(s): 関宏理
  • Organizer: 情報数理学シンポジウム2017
  • Place of Presentation: 大阪大学（大阪府吹田市）
  • Year and Date: 2017-01-27
  • Invited
• [Presentation] 不精密ルールを用いたファジィ推論による医療診断システムの構築 (2017)
  • Author(s): 山下亮, 関宏理, 乾口雅弘
  • Organizer: 第61回システム制御情報学会研究発表講演会論文集
• [Presentation] 関数重み付きファジィ推論モデル (2017)
  • Author(s): 関宏理
  • Organizer: 第33回ファジィシステムシンポジウム
• [Presentation] 動的な強調と抑制を考慮したSIRMs結合型ファジィ推論モデル (2017)
  • Author(s): 関宏理
  • Organizer: 第40回多値論理フォーラム
• [Presentation] 動的な強調と抑制を持つSIRMs結合型ファジィ推論モデルの性質 (2017)
  • Author(s): 関宏理
  • Organizer: 第27回インテリジェント・システム・シンポジウム
• [Presentation] 不精密ルールを考慮したラフ集合による知識獲得及びファジィ判別分析 (2017)
  • Author(s): 関宏理, 山下亮, 乾口雅弘
  • Organizer: 第12回コンピューテーショナル・インテリジェンス研究会
• [Presentation] OR演算型ファジィ推論モデルの性質と医療診断への応用 (2016)
  • Author(s): 関宏理
  • Organizer: 第32回ファジィシステムシンポジウム講演論文集
  • Place of Presentation: 佐賀大学（佐賀県佐賀市）
  • Year and Date: 2016-09-02
• [Presentation] Application to a Medical Diagnosis System by Extended Functional-Type SIRMs Connected Fuzzy Inference Method (2016)
  • Author(s): Diederik van Krieken, Hirosato Seki, and Masahiro Inuiguchi
  • Organizer: The 8th International Conference on Soft Computing and Intelligent Systems and the 17th International Symposium on Advanced Intelligent Systems
  • Place of Presentation: 北海学園大学（北海道札幌市）
  • Year and Date: 2016-08-26
  • Int'l Joint Research
• [Presentation] 論理和によるファジィ推論モデルの提案とその性質 (2015)
  • Author(s): 関宏理
  • Organizer: 第8回コンピューテーショナル・インテリジェンス研究会講演論文集
  • Place of Presentation: 県立広島大学（広島県広島市）
  • Year and Date: 2015-12-18
• [Presentation] ファジィ推論におけるMAX演算の性質とType-2ファジィ 推論モデルへの応用 (2015)
  • Author(s): 関宏理, 水本雅晴
  • Organizer: 計測自動制御学会システム・情報部門 学術講演会2015講演論文集
  • Place of Presentation: 函館アリーナ（北海道函館市）
  • Year and Date: 2015-11-20
• [Presentation] On the property of SIC fuzzy inference model with compatibility functions (2015)
  • Author(s): Hirosato Seki
  • Organizer: Integrated Uncertainty in Knowledge Modelling and Decision Making
  • Place of Presentation: Nha Trang, Vietnam
  • Year and Date: 2015-10-16
  • Int'l Joint Research
• [Presentation] ファジィシステムのこれまでとこれから ―Type-1ファジィシステムからType-2ファジィシステムへ― (2015)
  • Author(s): 関宏理
  • Organizer: 日本オペレーションズ・リサーチ学会 「数理的発想とその実践」研究部会第2回研究集会
  • Place of Presentation: 福井工業大学（福井県福井市）
  • Year and Date: 2015-07-25
• [Book] 人工知能学大事典 (2017)
  • Author(s): 関宏理、他（人工知能学会編）
  • Total Pages: 1600
  • Publisher: 共立出版
  • ISBN: 9784320124202