Neural Network to Solve Concave Games | Zendy

Zixin Liu | Zendy; Nengfa Wang | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Neural Network to Solve Concave Games

Author(s) -

Zixin Liu,

Nengfa Wang

Publication year - 2014

Publication title -

international journal of computer games technology

Language(s) - English

Resource type - Journals

eISSN - 1687-7055

pISSN - 1687-7047

DOI - 10.1155/2014/249721

Subject(s) - variational inequality , artificial neural network , stability (learning theory) , projection (relational algebra) , mathematics , computer science , mathematical economics , mathematical optimization , artificial intelligence , algorithm , machine learning

The issue on neural network method to solve concave games is concerned. Combined with variational inequality, Ky Fan inequality, and projection equation, concavegames are transformed into a neural network model. On the basis of the Lyapunov stable theory, some stability results are also given. Finally, two classic games’ simulation results are given toillustrate the theoretical results

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research