Establishment of generalized theory of self-organizing maps and its applications
| Project/Area Number |
17500193
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Bioinformatics/Life informatics
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
FURUKAWA Tetsuo Kyushu Institute of Technology, Graduate school of Life Science and Systems Engineering, Ph.D., Professor (50219101)
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| Project Period (FY) |
2005 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,270,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥270,000)
Fiscal Year 2007: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2006: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 2005: ¥1,500,000 (Direct Cost: ¥1,500,000)
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| Keywords | Neural network / Self-organizing map / Modular network / Manifold learning / Fiber bundle / Adaptive control / Autonomous robot / ニューラルネットワーク / 多様体 |
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| Research Abstract |
The purpose of this project is to establish the theory of generalized self-organizing maps and to apply it to various practical tasks. During the grant, we obtained the following results.
(1) The generalized theory of Kohonen's self-organizing map (SOM) was established for various data types. The theory was realized by adopting various module architectures as the replacement of the vector units of the conventional SOM, which we called mn SOM (modular network SOM). In the case of multi-layer perceptron module, theoretical equivalence was proved by using Legendre expansion in function space. We also established the theory of the higher-rank of SOM, that is called SOM^n. By using SOM^n, a set of data distributions are modeled by a fiber bundle.
(2) We applied the mn SOM and the SOM^n various practical tasks, such as adaptive control, texture classification, 3D object classification, face image recognition, hand written letter recognition and so on. We also applied them to mobile robots, and the robots succeeded to acquire an inner model of the outer environment.
(3) We developed some extended architectures based on these generalized SOMs. The self-evolving (i.e., self-growing) modular network was applied to mobile robots to organize a graph-base representation of the outer world.
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Report
(4 results)
Research Products
(172 results)
