TY - GEN

T1 - Solving the k-winners-take-all problem and the oligopoly cournot-nash equilibrium problem using the general projection neural networks

AU - Hu, Xiaolin

AU - Wang, Jun

PY - 2008

Y1 - 2008

N2 - The k-winners-take-all (k-WTA) problem is to select k largest inputs from a set of inputs in a network, which has many applications in machine learning. The Cournot-Nash equilibrium is an important problem in economic models . The two problems can be formulated as linear variational inequalities (LVIs). In the paper, a linear case of the general projection neural network (GPNN) is applied for solving the resulting LVIs, and consequently the two practical problems. Compared with existing recurrent neural networks capable of solving these problems, the designed GPNN is superior in its stability results and architecture complexity.

AB - The k-winners-take-all (k-WTA) problem is to select k largest inputs from a set of inputs in a network, which has many applications in machine learning. The Cournot-Nash equilibrium is an important problem in economic models . The two problems can be formulated as linear variational inequalities (LVIs). In the paper, a linear case of the general projection neural network (GPNN) is applied for solving the resulting LVIs, and consequently the two practical problems. Compared with existing recurrent neural networks capable of solving these problems, the designed GPNN is superior in its stability results and architecture complexity.

UR - http://www.scopus.com/inward/record.url?scp=54249147708&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-69158-7_73

DO - 10.1007/978-3-540-69158-7_73

M3 - Conference contribution

AN - SCOPUS:54249147708

SN - 3540691545

SN - 9783540691549

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 703

EP - 712

BT - Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers

T2 - 14th International Conference on Neural Information Processing, ICONIP 2007

Y2 - 13 November 2007 through 16 November 2007

ER -