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Path synthesis of four-bar mechanisms using synergy of polynomial neural network and Stackelberg game theory.
- Source :
-
Engineering Optimization . Jun2017, Vol. 49 Issue 6, p932-947. 16p. - Publication Year :
- 2017
-
Abstract
- In this article, a novel approach based on game theory is presented for multi-objective optimal synthesis of four-bar mechanisms. The multi-objective optimization problem is modelled as a Stackelberg game. The more important objective function, tracking error, is considered as the leader, and the other objective function, deviation of the transmission angle from 90° (TA), is considered as the follower. In a new approach, a group method of data handling (GMDH)-type neural network is also utilized to construct an approximate model for the rational reaction set (RRS) of the follower. Using the proposed game-theoretic approach, the multi-objective optimal synthesis of a four-bar mechanism is then cast into a single-objective optimal synthesis using the leader variables and the obtained RRS of the follower. The superiority of using the synergy game-theoretic method of Stackelberg with a GMDH-type neural network is demonstrated for two case studies on the synthesis of four-bar mechanisms. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ARTIFICIAL neural networks
*GMDH algorithms
*GAME theory
Subjects
Details
- Language :
- English
- ISSN :
- 0305215X
- Volume :
- 49
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Engineering Optimization
- Publication Type :
- Academic Journal
- Accession number :
- 122254018
- Full Text :
- https://doi.org/10.1080/0305215X.2016.1218641