▲ to top
Uehara Laboratory
Kiyohiko Uehara, Dr. Eng., Associate Professor
Ibaraki University, Japan
Research on Computational Intelligence
α-GEM Family, invented by Dr. K. Uehara:
New approaches for fuzzy inference and fuzzy rule learning
toward their use in artificial intelligence
The α-GEM family consists of the methods listed in the following table at present.
All of these methods were invented by Dr. K. Uehara.
|
Invented Method |
Meaning of the Method Name |
Year: 1st Proposal |
Main References |
(1) |
α-RITHM |
α-level-set and arithmetic-mean-based inference |
1995 |
[4] |
(2) |
α-GEM |
α-level-set and generalized-mean-based inference |
1997 |
[6] |
(3) |
α-GEMII |
α-level-set and generalized-mean-based inference with the proof of two-sided symmetry
of consequences |
2009 |
[8-12] |
(4) |
α-GEMS |
α-cut and generalized-mean-based inference in synergy with composition
| 2013 |
[17] |
(5) |
α-GEMST |
α-level-set and generalized-mean-based inference
in synergy with composition via linguistic-truth value control
| 2016 |
[19] |
(6) |
α-GEMMI |
α-level-set and generalized-mean-based inference with multi-level interpolation
| 2011 |
[13] |
(7) |
α-GEMMIET |
α-level-set and generalized-mean-based inference with multi-level interpolation extended
in the number of points for interpolation
| 2013 |
[16] |
(8) |
α-GEMILIE |
α-level-set and generalized-mean-based inference with infinite-level interpolation
| 2013 |
[15] |
(9) |
α-GEMINAS |
α-level-set and generalized-mean-based inference with fuzzy rule interpolation
at an infinite number of activating points
| 2015 |
[18] |
(10) |
α-GEMINAST |
α-level-set and generalized-mean-based inference in synergy
between α-GEMINAS and the composition via LTV control
| 2017 |
[21] |
(11) |
α-GEMI-ES |
α-GEMINAS-based local-evolution toward slight linearity for global smoothness
| 2017 |
[23] |
(12) |
α-GEMI-SING |
α-GEMII and α-GEMINAS-based local-evolution toward slight linearity
for global smoothness by using singleton input–output rules
| 2018 |
[24] |
(13) |
α-FUZZI-ES |
α-weight-based fuzzy-rule independent evaluations
| 2019 |
[26] |
(14) |
α-FUZZI-ES Learning |
α-FUZZI-ES-based fuzzy-rule learning
| 2019 |
[26] |
(15) |
α-FUZZI-EX |
α-weight-based fuzzy-rule independent evaluations extended for fuzzy inputs
| 2020 |
[25] |
(16) |
α-FUZZI-EX Learning |
α-FUZZI-EX-based fuzzy-rule learning
| 2020 |
[25] |
(17) |
α-GEMII-X Denoising |
α-GEMII-based denoising to unify fuzzy inference and preprocessing for fuzzy rule optimization
| 2021 |
[27] |
Relations Between the Methods in α-GEM Family
[22]
The α-GEM family consists of fuzzy inference methods for non-sparse rule base and sparse rule base
[22].
In addition, it includes methods for noise reduction and fuzzy rule learning
by which fuzzy rules can be efficiently optimized.
α-GEMII has played a central role in the growth of the α-GEM family.
α-GEMII
[8-12]: Fuzzy Inference Method
α-GEMII can mathematically prove the convexity of consequences
by the effective use of the generalized mean,
while the fuzzy constraints of given facts are propagated to those of the deduced consequences.
Moreover, it can mathematically prove the symmetricity of consequences
under some conditions that are axiomatically derived from the viewpoint of fuzzy inference
[8-12].
The operational process of the fuzziness-propagation control is not depicted in the figure for ease of visibility.
α-FUZZI-ES Learning
[26]: Fast Fuzzy-Rule Optimization
α-FUZZI-ES learning makes it possible to optimize fuzzy rules independently of each other.
This property reduces the dimensionality of the search space in finding the optimum fuzzy rules.
Thereby, α-FUZZI-ES learning can attain fast convergence in fuzzy rule optimization.
Moreover, α-FUZZI-ES learning can be efficiently performed with hardware in parallel
to optimize fuzzy rules independently of each other.
α-FUZZI-ES learning is especially effective when evaluation functions are not differentiable
and derivative-based optimization methods cannot be applied to fuzzy rule learning;
α-FUZZI-ES learning is superior in fast convergence, especially with the derivative-free optimization
of fuzzy rules
[26].
Demonstration of α-FUZZI-ES Learning: Application to Interval Prediction
[26]
The animation below shows the transitional changes of prediction intervals optimized by using α-FUZZI-ES learning;
Each fuzzy rule is tuned by an immune algorithm in the scheme of α-FUZZI-ES learning.
α-GEMI-ES
[23]: Noise Reduction for Fuzzy Rule Optimization
In the initial stage, fuzzy rules for α-GEMI-ES are set by directly using learning data.
Subsequently, α-GEMI-ES iteratively performs α-GEMINAS and reduces noise in each iteration.
α-GEMI-ES provides a unified platform for fuzzy inference and fuzzy rule learning with noise-corrupted data.
It is expected to prevent fuzzy rules from overfitting to noise in learning data,
especially when only a small amount of learning data is available for fuzzy rule optimization
in comparison with the number of fuzzy rules
[23].
Demonstration of α-GEMI-ES in Reducing Noise
[23]
The animation below shows the transitional changes of noise reduction by α-GEMI-ES.
Main Papers
More papers are listed
here.
-
K. Uehara, E. Taguchi, T. Watahiki, and T. Miyata, “A Data Converter
for Analog/Fuzzy-Logic Interface,” The Trans. of the Institute
of Electronics and Communication Engineers, Vol.J67-C, No.4,
pp. 391-396, 1984 (in English
and
in Japanese).
-
K. Uehara and M. Fujise,
“Fuzzy Inference Based on Families of α-Level Sets,”
IEEE Transactions on Fuzzy Systems, Vol. 1, No. 2, pp. 111–124, 1993.
-
K. Uehara and M. Fujise,
“Multistage Fuzzy Inference Formulated as Linguistic-Truth-Value Propagation and
its Learning Algorithm Based on Back-Propagating Error Information,”
IEEE Transactions on Fuzzy Systems, Vol. 1, No. 3, pp. 205–221, 1993.
-
K. Uehara, “Fuzzy Inference Based on a Weighted Average of Fuzzy
Sets and its Learning Algorithm for Fuzzy Exemplars,” Proc. of the International
Joint Conference of the Fourth IEEE International Conference
on Fuzzy Systems and the Second International Fuzzy Engineering
Symposium (FUZZ-IEEE/IFES’95), Vol. IV, pp. 2253–2260, 1995.
-
K. Uehara and K. Hirota,
“Fuzzy Connection Admission Control for ATM Networks
Based on Possibility Distribution of Cell Loss Ratio,”
IEEE Journal on Selected Areas in Communications, Vol. 15, No. 2, pp. 179–190, 1997.
-
K. Uehara and K. Hirota, “Parallel Fuzzy Inference Based on α-Level
Sets and Generalized Means,” Information Sciences (International Journal),
Vol. 100, No. 1–4, pp. 165–206, 1997.
-
K. Uehara and K. Hirota,
“Parallel and Multistage Fuzzy Inference Based on Families of α-Level Sets,”
Information Sciences (International Journal ), Vol. 106, No. 1-2, pp. 159–195, 1998.
-
K. Uehara, T. Koyama, and K. Hirota, “Fuzzy Inference with
Schemes for Guaranteeing Convexity and Symmetricity in Consequences
Based on α-Cuts,”
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 13, No. 2, pp. 135–149, 2009.
-
K. Uehara, T. Koyama, and K. Hirota, “Inference with Governing
Schemes for Propagation of Fuzzy Convex Constraints Based on α-Cuts,”
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 13,
No. 3, pp. 321–330, 2009.
-
K. Uehara, T. Koyama, and K. Hirota,
“Inference Based on α-Cut and Generalized Mean with Fuzzy Tautological Rules,”
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 14, No. 1, pp. 76–88, 2010.
-
K. Uehara, T. Koyama, and K. Hirota,
“Suppression Effect of α-Cut Based Inference on Consequence Deviations,&dquo;
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 14, No. 3, pp. 256–271, 2010.
-
K. Uehara, T. Koyama, and K. Hirota,
“Inference Based on α-Cut and Generalized Mean in Representing Fuzzy-Valued Functions,”
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 14, No. 6, pp. 581–592. 2010.
-
K. Uehara, S. Sato, and K. Hirota, “Inference for Nonlinear Mapping
with Sparse Fuzzy Rules Based on Multi-Level Interpolation,”
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 15, No. 3,
pp. 264–287, 2011.
-
K. Tsukamoto and K. Uehara,
“A Map Matching Method with Fuzzy Inference”
IEEJ Transactions on Electronics, Information and Systems, Vol. 132, No. 2, 334-335, 2012
(in Japanese).
-
K. Uehara and K. Hirota, “Infinite-Level Interpolation for Inference
with Sparse Fuzzy Rules: Fundamental Analysis Toward Practical Use,”
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 17,
No. 1, pp. 44–59, 2013.
-
K. Uehara and K. Hirota, “Multi-Level Interpolation for Inference
with Sparse Fuzzy Rules: An Extended Way of Generating Multi-Level
Points,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 17,
No. 2, pp. 127–148, 2013.
-
K. Uehara and K. Hirota, “Multi-Level Control of Fuzzy-Constraint
Propagation in Inference Based on α-Cuts and Generalized Mean,”
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 17, No. 4,
pp. 647–662, 2013.
-
K. Uehara and K. Hirota, “Inference with Fuzzy Rule Interpolation
at an Infinite Number of Activating Points,”
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 19, No. 1, pp. 74–90, 2015.
-
K. Uehara and K. Hirota, “Multi-Level Control of Fuzzy Constraint
Propagation via Evaluations with Linguistic Truth Values
in Generalized-Mean-Based Inference,”
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 20, No. 2, pp. 355–377, 2016.
-
K. Uehara and K. Hirota,
“Fuzzy Inference: Its Past and Prospects,”
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 21, No. 1, pp. 13–19, 2017.
-
K. Uehara and K. Hirota, “Multi-Level Control of Fuzzy-Constraint Propagation
in Inference with Fuzzy Rule Interpolation
at an Infinite Number of Activating Points,”
Journal of Advanced Computational Intelligence and Intelligent Informatics,
Vol. 21, No. 3, pp. 425–447, 2017.
-
K. Uehara and K. Hirota,
“Fuzzy Inference Based on α-Cuts and Generalized Mean:
Relations Between the Methods in its Family and their Unified Platform,”
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 21, No. 4, pp. 597–615, 2017.
-
K. Uehara and K. Hirota, “Noise Reduction with Inference Based on Fuzzy Rule Interpolation
at an Infinite Number of Activating Points:
Toward fuzzy rule learning in a unified inference platform,”
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 22, No. 6, pp. 883–899, 2018.
-
K. Uehara and K. Hirota, “Noise Reduction with Fuzzy Inference
Based on Generalized Mean and Singleton Input–Output Rules:
Toward fuzzy rule learning in a unified inference platform,”
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 23, No. 6, pp. 1027–1043, 2019.
-
K. Uehara and K. Hirota,“Independent Evaluations of Each Fuzzy Rule
for Derivative-Free Optimization of Fuzzy Systems:
Toward Fast Fuzzy-Rules Learning for Fuzzy Inputs,”
Proceedings of the 9th International Symposiumon Computational Intelligence and IndustrialApplications (ISCIIA2020), 1A2-2-4, pp. 1–8, 2020.
-
K. Uehara and K. Hirota,
“A Fast Method for Fuzzy Rules Learning with Derivative-Free Optimization
by Formulating Independent Evaluations of Each Fuzzy Rule,”
Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 25, No. 2, pp. 213–225, 2021.
-
K. Uehara,“Noise Reduction by Fuzzy Inference
Based on α-Cuts and Generalized Mean: A Feasibility Study,”
Proceedings of the 7th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2021),
M2-6-5, pp. 1–7, 2021.
-
K. Uehara,“Continual Learning with Fuzzy Inference
Based on Derivative-Free Independent Optimization
of Each Fuzzy Rule: A Feasibility Study,”
Proceedings of Continual Learning and Emergence of Intelligent Systems,
Japan Society for Fuzzy Theory and Intelligent Informatics,
p. 23, Dec. 2023.
More papers are listed
here.
Awards
|
Award |
Journal/Conference Name |
(1) |
JACIII Best Paper Award 2011 |
Journal of Advanced Computational Intelligence and Intelligent Informatics |
(2) |
ISCIIA2016 Best Paper Award |
The 7th International Symposiumon Computational Intelligence and Industrial Applications
(ISCIIA2016), 2016. |
(3) |
ISCIIA2016 Session Best Presentation Award |
The 7th International Symposiumon Computational Intelligence and Industrial Applications
(ISCIIA2016), 2016. |
(4) |
IWACIII2017 Session Best Presentation Award |
The 5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics
(IWACIII2017), 2017. |
(5) |
ISCIIA-ITCA2018 Session Best Presentation Award |
The 8th International Symposiumon Computational Intelligence and IndustrialApplications (ISCIIA2018),
The 12th China-Japan International Workshop on Information Technology and Control Applications (ITCA2018),
2018. |
(6) |
IWACIII2019 Session Best Presentation Award |
The 6th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2019),
2019. |
(7) |
JACIII Outstanding Reviewer Award 2019 |
Journal of Advanced Computational Intelligence and Intelligent Informatics |
(8) |
ISCIIA2020 Session Best Presentation Award |
The 9th International Symposiumon Computational Intelligence and Industrial Applications (ISCIIA2020), 2020. |
Main Recent Activities
Year |
Official Position |
Institute/Conference Name |
2021-present |
Councilor (評議員) |
Japan Society for Fuzzy Theory and Intelligent Informatics
(日本知能情報ファジィ学会) |
2019-2021 |
Director (理事, 広報委員長) |
Japan Society for Fuzzy Theory and Intelligent Informatics
(日本知能情報ファジィ学会) |
2004-present |
Editorial Member |
Journal of Advanced Computational Intelligence and Intelligent Informatics
|
2021 |
Chairperson |
The 7th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2021)
|
2021 |
Program Committee Member |
The 7th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2021)
|
2017-2020 |
Vice Editor-in-Chief for Papers (論文副委員長) |
Editorial Board, Japan Society for Fuzzy Theory and Intelligent Informatics
(日本知能情報ファジィ学会 会誌編集委員会)
|
2020 |
Chairperson |
The 9th International Symposiumon Computational Intelligence and Industrial Applications (ISCIIA2020)
|
2020 |
Program Committee Member |
The 9th International Symposiumon Computational Intelligence and Industrial Applications (ISCIIA2020)
|
2020 |
Program Committee Member |
Joint 11th International Conference on Soft Computing and Intelligent Systems and
21st International Symposium on Advanced Intelligent Systems (SCIS&ISIS2020)
|
2019 |
Program Committee Member |
The 6th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2019)
|
2018 |
Program Committee Member |
The 8th International Symposiumon Computational Intelligence and IndustrialApplications (ISCIIA2018),
The 12th China-Japan International Workshop on Information Technology and Control Applications (ITCA2018)
|
2017 |
Program Committee Member |
The 5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2017)
|
2016 |
Program Committee Member |
The 7th International Symposiumon Computational Intelligence and Industrial Applications (ISCIIA2016)
|
2017 |
Associate Editor |
Journal of Advanced Computational Intelligence and Intelligent Informatics
|
2013 |
Guest Editor |
Journal of Advanced Computational Intelligence and Intelligent Informatics
|
Profile in Japanese:
上原 清彦
茨城大学大学院 理工学研究科 准教授, 博士(工学)[東京工業大学 授与]
研究分野:ファジィ理論、ファジィ推論、ファジィ論理、ファジィルール学習、ファジィ推論による人工知能、人工免疫システム
職務経験:
- 東京芝浦電気株式会社(現 株式会社 東芝), 総合研究所
- ATR光電波通信研究所
- 株式会社 東芝, 関西研究所
- 株式会社 東芝, 研究開発センター 情報・通信システム研究所
- 株式会社 東芝, 研究開発センター 通信プラットホームラボラトリー
- 茨城大学大学院 理工学研究科 メディア通信工学専攻(博士前期課程), 工学部 メディア通信工学科
- 茨城大学大学院 理工学研究科 電気電子システム工学専攻(博士前期課程), 工学部 電気電子システム工学科
- 茨城大学大学院 社会インフラシステム科学専攻(博士後期課程)
Copyright © Kiyohiko Uehara