中文摘要：

改进Hopfield神经网络（HNN）的激活函数可以提高网络的抗噪能力，但是其收敛速度会大大降低。为了解决改进激活函数后HNN收敛速度较慢的问题，文中提出一种基于双SigmoidHo曲eld神经网络（DSHNN）的盲检测算法。该算法不仅继承了HNN所有的优点，还极大地提高了算法的收敛速度，缩短了运行时间。仿真实验表明，DSHNN算法比HNN算法的抗干扰性能略强，且其能量函数的收敛速度大大提升。

英文摘要：

Improving the Hopfield Neural Networks＇ （HNN） activation function could enhance this network＇ s immunity, but its conver- gence speed was greatly reduced. In order to solve the problem of slow convergence of HNN after activation function improved, applied the Double Sigmoid Hopfield Neural Networks （DSHNN） for blind detection. This algorithm not only inherited all the advantages of the HNN,but also greatly improved the convergence speed and shortened the running time. Simulation results show that, the DSHNN had stronger anti-interference performance than the HNN slightly and enhanced the convergence rate of the energy function greatly.