| Project/Area Number |
04650301
|
|---|
| Research Category |
Grant-in-Aid for General Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
情報工学
|
|---|
| Research Institution | IBARAKI UNIVERSITY |
|---|
Principal Investigator |
MATSUYAMA Yasuo IBARAKI UNIVERSITY, DEPARTMENT OF COMPUTER AND INFORMATION SCIENCES PROFESSOR, 工学部, 教授 (60125804)
|
|---|
| Project Period (FY) |
1992 – 1993
|
| Project Status |
Completed (Fiscal Year 1993)
|
| Budget Amount *help |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 1993: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 1992: ¥1,100,000 (Direct Cost: ¥1,100,000)
|
|---|
| Keywords | Heterogeneous parallelism / Connectionism / Massive parallelism / Learning / Multiple criteria optimization / log-bias / Mutation / Harmonic competition / 教師あり学習 / 教師なし学習 / ダイバージェンス / 二重並列性 / 相利共生 / 競合学習 / ニューラルネット / 計算論的学習 / 自己組織化 |
|---|
| Research Abstract |
This study has a dual purpose : Designing an emulator which realizes symbiosis of heterogeneous parallelisms and presenting new connectionst learning algorithms. On the realization of the emulator, two workstations are used. One is for an SIMD mechanism where a finegrained parallelism is emulated. The other is for a coarse-grained parallelsm which controls the massive parallel part. KL1 was used for this control mechanism. The multiply descent cost competitive learning algorithm was run on this symbiotic system. The nondeterminism caused by the parallelsm was found to be rather meritorious for the exit from bad local minima.
For the developement of new learning algorithms, the head investigator presented two major new methods. On the supervised learning, the backpropagation with additional penalties was presented. This algorithm includes entropy/divergence penalties on the weithts and outputs. Pruning of the network and improvement of errors and generalization were acheived.
On the unsupervised case, the head investigator created the harmonic competitive learning. This algorithm enables to solve multiple criteria optimization with the aid of self-organization. The logarithmic competition bias and the logarithmic weight mutation solved the local optimality in the case of data compression.
Thus, this research project was completed by accomplishing the claimed results.
|
