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In this paper, the K-Winners-Take-All (KWTA) problem is formulated equivalently to a linear program. A recurrent neural network for KWTA is then proposed for solving the linear programming problem. The KWTA network is globally convergent to the optimal solution of the KWTA problem. Simulation results are further presented to show the effectiveness and(More)
We propose a new algorithm based on a chaotic neural network to solve the attributed relational graph matching problem, which is an NP-hard problem of prominent importance in pattern recognition research. From some detailed analyses, we reach the conclusion that, unlike the conventional Hopfield neural networks for the attributed relational graph matching(More)
  • Shenshen Gu
  • 2010 International Conference on Wireless…
  • 2010
In the field of signal processing, many problems can be formulated as optimization problems. And most of these optimization problem can be further described in a formal form, that is binary quadratic programming problem(BQP). However, solving the BQP is proved to be NP-hard. Due to this reason, many novel algorithms have been proposed in order to improve(More)
In this paper, we proposed a recurrent neural network to compute the distance between a point to an ellipsoid in n spatial dimensions. So far, the problem used to be solved by traditional mathematical algorithms, which is either too slow in computing time or too one-sided in applications. Our proposed neural network, which makes use of a cost gradient(More)
Binary quadratic programming (BQP) is a typical integer programming problem widely applied in the field of signal processing, economy, management and engineering. However, it's NP-hard and lacks efficient algorithms. Due to this reason, in this paper, a novel polynomial algorithm to linearly constrained binary quadratic programming problems with Q being a(More)