1992 Fiscal Year Final Research Report Summary
Difference in Learning ability of Neural Nets and Logical Expressions
| Project/Area Number |
03452191
|
|---|
| Research Category |
Grant-in-Aid for General Scientific Research (B)
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
計測・制御工学
|
|---|
| Research Institution | National Institute for Environmental Studies |
|---|
Principal Investigator |
ICHIKAWA Atsunobu National Institute for Environmental Studies, Director General, 所長 (60016714)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
NAKAMURA Kiyohiko Tokyo Institute of Technology, Department of Intelligence Science, Associate Pro, 総合理工, 助教授 (10172397)
|
|---|
| Project Period (FY) |
1991 – 1992
|
| Keywords | Neural nets / Petri nets / Learning capability / 可達集合 |
|---|
| Research Abstract |
The purpose of this study is to identify the difference between the neural nets and the logical expressions in the learning ability for the artificial intelligence.
The origin of capability of detecting the time difference smaller than the pulse duration used in the neural nets is first analyzed. The parallel processing mechanism which enables the above detection is proposed and verified by the computer simulation. It is necessary for the neural nets to have selflearning capability to generate wide variety of different sequences and capability of identifying the adequate sequence from the sequences. The mechanism for these capability is analyzed and proposed as a neural net model. The computer simulation of this model in the realistic environment shows that the proposed model is sufficient for generating the sequences and selecting a particular sequence.
A class of Petri nets, which is a typical expression of the logical type, is proposed for the reachability set of the net is to be identified without carrying out the execution of the nets. In other words, the reachability set is identified for the class of the nets from the structure of the nets and the initial states.
Combination of these two understandings makes us clear that the time differentiability is the most crucial to give advantage of the neural nets over the logical expressions.
|
