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- Shenshen Gu, Jun Wang
- 2007 International Joint Conference on Neural…
- 2007

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)

- Shenshen Gu, Songnian Yu
- Proceedings of 2004 International Symposium on…
- 2004

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)

- Duan Li, Xiaoling Sun, Jianjun Gao, Shenshen Gu, Xiaojin Zheng
- Automatica
- 2011

Reachability is one of the most important behavioral properties of Petri nets. We propose in this paper a novel approach for solving the fundamental equation in the reachability analysis of acyclic Petri nets, which has been known to be NP-complete. More specifically, by adopting a revised version of the cell enumeration method for an arrangement of… (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)

- Shenshen Gu, Jiao Peng, Rui Cui
- ISNN
- 2014

- Shenshen Gu, Jiao Peng
- 2015 Seventh International Conference on Advanced…
- 2015

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)

- Shenshen Gu, Wentao Zhang, Fei Wang, Lili Zhang, Zhijian Chen
- 2013 IEEE 8th Conference on Industrial…
- 2013

The measuring error in high precision electronic balance mainly results from the nonlinear error of weighting sensor. To deal with this problem, a novel adaptive segmenting best-fitting method is proposed in this paper to reduce the nonlinear error in the weighting sensor. A resistance strain type electronic balance manufactured by Shanghai Jing Tian… (More)

- Shenshen Gu, Rui Cui
- Neurocomputing
- 2015

- Shenshen Gu
- ISNN
- 2013

- Shenshen Gu
- ISNN
- 2005