用带非线性自反馈的神经网络求解最大团问题

Using a Neural Network with Nonlinear Self-feedback to Solve the Maximum Clique Problem

针对饱和非线性动态网络算法(SLDN算法)解最大团问题容易陷入局部最优这一缺点,提出了解决该问题的一种新的神经网络算法,并构建了新数学模型。该算法在SLDN算法基础上加入了非线性自反馈,具备良好的动力学特性。分析了加入非线性自反馈后的收敛性,并且通过仿真实验表明其整体性能要优于SLDN算法。

As the MCP is NP-hard, an efficient approach to treating this problem is to design appropriate recurrent neural networks. We develop a new algorithm for the MCP, which can, to a certain extent, prevent the associated neural network from falling into local optimal points. The proposed algorithm incorporates nonlinear self-feedback into the SLDN algorithm and has distinguished dynamical characteristics. Simulation results show that the performance of proposed algorithm is statistically superior to the SLDN algorithm.