Genetic neural networks to approximate feedback Nash equilibria in dynamic games
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Publication:1879556
DOI10.1016/S0898-1221(03)90186-6zbMath1116.91308MaRDI QIDQ1879556
Sibel Sirakaya, Nedim M. Alemdar
Publication date: 23 September 2004
Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)
Noncooperative games (91A10) Learning and adaptive systems in artificial intelligence (68T05) Dynamic games (91A25)
Related Items (3)
Feedback approximation of the stochastic growth model by genetic neural networks ⋮ Optimal time aggregation of infinite horizon control problems ⋮ Coefficient estimation of IIR filter by a multiple crossover genetic algorithm
Cites Work
- Unnamed Item
- Tractable classes of nonzero-sum open-loop Nash differential games: Theory and examples
- On-line computation of Stackelberg equilibria with synchronous parallel genetic algorithms
- A genetic game of trade, growth and externalities
- A class of differential games for which the closed-loop and open-loop Nash equilibria coincide
- Dynamic noncooperative game theory
- On nonunique closed-loop Nash equilibria for a class of differential games with a unique and degenerated feedback solution
- Multilayer feedforward networks are universal approximators
- Three-country trade relations: A discrete dynamic game approach
- Identification of classes of differential games for which the open loop is a degenerate feedback Nash equilibrium
- A class of trilinear differential games
- Discrete-Time Finite Horizon Approximation of Infinite Horizon Optimization Problems with Steady-State Invariance
- Numerical solution of dynamic oligopoly games with capital investment.
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