Fuzzy inference based on families of α-level sets. - Kiyohiko Uehara; Masayuki Fujise (IEEE Trans. Fuzzy Syst., 1993)

Multistage fuzzy inference formulated as linguistic-truth-value propagation and its learning algorithm based on back-propagating error information. - Kiyohiko Uehara; Masayuki Fujise (IEEE Trans. Fuzzy Syst., 1993)

Parallel Fuzzy Inference Based on alpha-Level Sets and Generalized Means. - Kiyohiko Uehara; Kaoru Hirota (Inf. Sci., 1997)

Fuzzy Inference Based Connection Admission Control in ATM Networks. - Kiyohiko Uehara; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 1997)

Fuzzy Connection Admission Control for ATM Networks Based on Possibility Distribution of Cell Loss Ratio. - Kiyohiko Uehara; Kaoru Hirota (IEEE J. Sel. Areas Commun., 1997)

Parallel and Multistage Fuzzy Inference Based on Families of alpha-level sets. - Kiyohiko Uehara; Kaoru Hirota (Inf. Sci., 1998)

Fuzzy Inference with Schemes for Guaranteeing Convexity and Symmetricity in Consequences Based on alpha-Cuts. - Kiyohiko Uehara; Takumi Koyama; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2009)

Inference with Governing Schemes for Propagation of Fuzzy Convex Constraints Based on alpha-Cuts. - Kiyohiko Uehara; Takumi Koyama; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2009)

Inference Based on alpha-Cut and Generalized Mean with Fuzzy Tautological Rules. - Kiyohiko Uehara; Takumi Koyama; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2010)

Suppression Effect of alpha-Cut Based Inference on Consequence Deviations. - Kiyohiko Uehara; Takumi Koyama; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2010)

Inference Based on ά-Cut and Generalized Mean in Representing Fuzzy-Valued Functions. - Kiyohiko Uehara; Takumi Koyama; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2010)

Inference for Nonlinear Mapping with Sparse Fuzzy Rules Based on Multi-Level Interpolation. - Kiyohiko Uehara; Shun Sato; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2011)

Infinite-Level Interpolation for Inference with Sparse Fuzzy Rules: Fundamental Analysis Toward Practical Use. - Kiyohiko Uehara; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2013)

Multi-Level Interpolation for Inference with Sparse Fuzzy Rules: An Extended Way of Generating Multi-Level Points. - Kiyohiko Uehara; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2013)

Multi-Level Control of Fuzzy-Constraint Propagation in Inference Based on α-Cuts and Generalized Mean. - Kiyohiko Uehara; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2013)

Inference with Fuzzy Rule Interpolation at an Infinite Number of Activating Points. - Kiyohiko Uehara; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2015)

Multi-Level Control of Fuzzy-Constraint Propagation via Evaluations with Linguistic Truth Values in Generalized-Mean-Based Inference. - Kiyohiko Uehara; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2016)

Fuzzy Inference: Its Past and Prospects. - Kiyohiko Uehara; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2017)

Multi-Level Control of Fuzzy-Constraint Propagation in Inference with Fuzzy Rule Interpolation at an Infinite Number of Activating Points. - Kiyohiko Uehara; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2017)

Fuzzy Inference Based on α-Cuts and Generalized Mean: Relations Between the Methods in its Family and their Unified Platform. - Kiyohiko Uehara; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2017)

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. - Kiyohiko Uehara; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2018)

Noise Reduction with Fuzzy Inference Based on Generalized Mean and Singleton Input-Output Rules: Toward Fuzzy Rule Learning in a Unified Inference Platform. - Kiyohiko Uehara; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2019)

A Fast Method for Fuzzy Rules Learning with Derivative-Free Optimization by Formulating Independent Evaluations of Each Fuzzy Rule. - Kiyohiko Uehara; Kaoru Hirota (J. Adv. Comput. Intell. Intell. Informatics, 2021)