| Project/Area Number |
15K00333
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Soft computing
|
|---|
| Research Institution | University of Fukui |
|---|
Principal Investigator |
Murase Kazuyuki 福井大学, 学術研究院工学系部門, 教授 (40174289)
|
|---|
| Research Collaborator |
HATA Ryusuke 福井大学, 大学院工学研究科
M.A.H. Akhand Kuhlna University of Engineering and Technology, Professor
Pintu Chandra Shill Kuhlna University of Engineering and Technology, Professor
Md. Monirul Islam Bangladesh University of Engineering and Technology, Professor
|
|---|
| Project Period (FY) |
2015-04-01 – 2018-03-31
|
| Project Status |
Completed (Fiscal Year 2017)
|
| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2017: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
|
|---|
| Keywords | ニューロファジィ / 多元数 / クラス分類 / 関数近似 / 深層学習 / オートエンコーダー / 畳み込みニューラルネットワーク / クラス分類問題 / 関数近似問題 |
|---|
| Outline of Final Research Achievements |
When computer tells you what the object is in a photograph, it is essential to know how and why the computer determined it. In the Neuro-Fuzzy, the decision is made with a combination of rules. In Neuro-Fuzzy learning algorithm, in contrast, the rules are generated from a training set of the given data. With the conventional methods, however, a large number of rules are generated and it is hard to understand. In this study, by using complex numbers and quaternions, new algorithms were proposed to reduce the total number of parameters to be determined during training. In real-world benchmark data, the multi-valued versions of Neuro-Fuzzy with reduced number of parameters converged much faster exhibiting equivalent or better accuracy than the real-valued counterpart. Understanding rules will be thus easier for given problems.
|
