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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP | 2024-05-09 15:05 |
Kagawa | Kagawa Prefecture Social Welfare Center | A Gray Wolf Optimization Method with Adaptive Large Neighborhood Search for the Traveling Salesman Problems Rikuto Shibutani, Takayuki Kimura (NIT)  NLP2024-5 |
In solving the Traveling Salesman Problem (TSP), the Gray Wolf Optimization method has shown better performance, but it ... [more] | NLP2024-5 pp.19-24 |
| CCS | 2024-03-27 14:00 |
Hokkaido | RUSUTSU RESORT | Evaluation of recurrent neural network training using multi-phase quantization optimizer Hiiro Yamazaki, Itsuki Akeno, Koki Nobori, Tetsuya Asai, Kota Ando (Hokkaido Univ.) CCS2023-44 |
In this research, we apply "Holmes", an optimizer dedicated to edge training of neural networks, to recurrent neural net... [more] | CCS2023-44 pp.30-35 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM [detail] | 2023-11-15 15:30 |
Kumamoto | Civic Auditorium Sears Home Yume Hall (Primary: On-site, Secondary: Online) |
* Itsuki Akeno, Hiro Yamazaki, Tetsuya Asai, Kota Ando (Hokkaido Univ) VLD2023-41 ICD2023-49 DC2023-48 RECONF2023-44 |
We propose a processor architecture for neural network (NN) training in edge and prototype it on an FPGA (Field--Program... [more] | VLD2023-41 ICD2023-49 DC2023-48 RECONF2023-44 pp.64-69 |
| CCS | 2022-03-27 10:25 |
Hokkaido | RUSUTSU RESORT HOTEL & CONVENTION (Primary: On-site, Secondary: Online) |
A novel hardware-oriented log-quantized optimizer for edge AI devices and their online learning Tatsuya Kaneko, Yoshiharu Yamagishi, Hiroshi Momose, Tetsuya Asai (Hokkaido Univ.) CCS2021-39 |
In recently, the concept of training neural networks (NN) at the edge has attracted much attention. Updating parameters... [more] |
CCS2021-39 pp.19-24 |
| NC, MBE (Joint) |
2021-03-05 14:30 |
Online | Online | Adaptive Optimization Method in Artificial Neural Network that Independ on Learning Rate Tetsuya Sato, Yukari Yamauti (Nihon Univ.) NC2020-72 |
What kind of optimizer is used in machine learning is an important issue. SGD has high accuracy but slow convergence and... [more] | NC2020-72 pp.169-173 |
| NLP | 2017-05-11 16:25 |
Okayama | Okayama University of Science | I-PD Controller Design Using Particle Swarm Optimizer
-- Settling Time Minimization Under Constraint of the Gain Crossover Frequency -- Yuzo Ohta (Kobe Univ.) NLP2017-14 |
In this paper, we consider the parameter tuning of I-PD controller which achieves minimum settling time control under th... [more] | NLP2017-14 pp.69-72 |
| CAS, NLP | 2016-10-27 09:55 |
Tokyo | Parameter Optimization for Power Line Communications Considering Operational Status of Electrical Appliances Shunsuke Yasui, Takeshi Kamio, Ena Kono, Hisato Fujisaka (Hiroshima City Univ.) CAS2016-39 NLP2016-65 |
Power line communication (PLC) is considered as one of communication systems to support a smart grid. Especially, PLC ha... [more] | CAS2016-39 NLP2016-65 pp.5-10 |
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| NLP | 2016-05-27 10:15 |
Kochi | Kochi Univ. | Implementation of parallel particle swarm optimizers on PC cluster Hidehiro Nakano, Ryosuke Matsumoto, Tomoyuki Sasaki, Arata Miyauchi (Tokyo City Univ.) NLP2016-13 |
This paper proposes an implementation method of the particle swarm optimizer (PSO) to PC cluster. In PSO, many particles... [more] | NLP2016-13 pp.63-66 |
| NLP | 2016-03-25 10:00 |
Kyoto | Kyoto Sangyo Univ. | Implementation of a specific processor for particle swarm optimizer networks Atsushi Moriyasu, Hidehiro Nakano, Arata Miyauchi (Tokyo City Univ.) NLP2015-150 |
This paper attempts to implement a processor specialized for particle swarm optimizer networks on FPGA. In the particle ... [more] | NLP2015-150 pp.47-51 |
| NLP | 2015-04-24 16:15 |
Kagawa | Kagawa Social Welfare Center | Design of a processing unit for particle swarm optimizers Hidehiro Nakano, Yasuhiro Shimada, Arata Miyauchi (Tokyo City Univ.) NLP2015-26 |
In this paper, a processor which realizes the parallel calculations of Particle Swarm Optimizer (PSO) is proposed. As th... [more] | NLP2015-26 pp.127-131 |
| NLP | 2014-05-27 10:55 |
Shimane | Big Heart IZUMO | Search of Multiple Solutions and the Number of Solutions by the Particle Swarm Optimizer Takuya Takemura, Takumi Sato, Toshimichi Saito (Hosei Univ.) NLP2014-10 |
This paper studies a particle swarm optimizer for multi-solution problems in the case where the number of solutions is... [more] |
NLP2014-10 pp.47-50 |
| NC, NLP | 2013-01-24 16:20 |
Hokkaido | Hokkaido University Centennial Memory Hall | Insensitive Particle Swarm Optimizers and Application to Exploring Periodic Points Kazuki Maruyama, Toshimichi Saito (Hosei Univ.) NLP2012-119 NC2012-109 |
This paper studies an insensitive particle swarm optimizer (IPSO) for multi-solution problems. The IPSO is governed by... [more] |
NLP2012-119 NC2012-109 pp.87-91 |
| NC | 2012-10-05 11:30 |
Fukuoka | Kyushu Institute of Technology (Wakamatsu Campus) | Effects of Collision and Insensitivity in Particle Swarm Optimizers for Multi-solution Problmes Kazuki Maruyama, Toshimichi Saito (Hosei Univ.) NC2012-52 |
This paper studies flexible particle swarm optimizer for multi-solution problems. The system is governed by deterministi... [more] | NC2012-52 pp.91-96 |
| MBE, NC (Joint) |
2012-03-16 10:40 |
Tokyo | Tamagawa University | Application of Population-based Optimizers to Exploring the Maximum Power Point Masaya Muraoka, Noriaki Mikami, Toshimichi Saito (HU) NC2011-181 |
This paper studies application of population-based optimizers to exploring maximum power points in potovoltaic systems. ... [more] |
NC2011-181 pp.353-358 |
| NLP | 2012-01-23 16:40 |
Fukushima | Aizu-keiko-do Hall | Insensitive Particle Swarm Optimizers and Multi-solution Problems Kazuki Maruyama, Ryosuke Sano, Toshimichi Saito (HU) NLP2011-132 |
This paper presents a novel particle swarm optimizer for multi-solution problems. The algorithm has several characteri... [more] |
NLP2011-132 pp.47-50 |
| CAS, NLP | 2011-10-21 16:55 |
Shizuoka | Shizuoka Univ. | Exploring Maximum Power Point by Particle Swarm Optimization Masaya Muraoka, Toshimichi Saito (HU) CAS2011-61 NLP2011-88 |
This paper studies application of particle swarm potimizers (PSO) to exploring maximum power points (MPP) in potovoltai... [more] |
CAS2011-61 NLP2011-88 pp.163-168 |
| NC, MBE (Joint) |
2011-03-08 14:10 |
Tokyo | Tamagawa University | The Search Performance of Multiple Particle Swarm Optimizers with Inertia Weight with Diversive Curiosity Hong Zhang (K.I.T.) NC2010-177 |
In this paper we propose a newly multiple particle swarm optimizers with inertia weight with diversive curiosity (MPSOIW... [more] | NC2010-177 pp.295-300 |
| NC, NLP | 2009-07-14 11:30 |
Nara | NAIST | Growing particle swarm optimizer with a population-dependent parameter Chihiro Kurosu, Toshimichi Saito (Hosei Univ.), Kenya Jin'no (Nippon Inst. of Tech.) NLP2009-32 NC2009-25 |
Particle swarm optimizer with growing structure is considered in this paper. If a particle exploring the optimum is ... [more] |
NLP2009-32 NC2009-25 pp.99-103 |
| NC, MBE (Joint) |
2009-03-12 16:05 |
Tokyo | Tamagawa Univ. | Effects of Canonical Particle Swarm Optimizer with Diversive Curiosity Hong Zhang, Masumi Ishikawa (Kyushu Inst. of Tech.) NC2008-137 |
This paper proposes a new method, named Canonical Particle Swarm Optimizer with Diversive Curiosity (CPSO/DC), which is ... [more] | NC2008-137 pp.201-206 |
| NC, MBE (Joint) |
2008-03-14 14:50 |
Tokyo | Tamagawa Univ | Model Selection of Canonical Particle Swarm Optimizer by EPSO: Meta-Optimization Hong Zhang, Masumi Ishikawa (Kyushu Inst. of Tech.) NC2007-196 |
We have proposed Evolutionary Particle Swarm Optimization, EPSO, which can estimate PSO models for efficiently solving v... [more] | NC2007-196 pp.495-500 |
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