基于CS的Hopfield神经网络数字识别应用

Application of Digit Recognition Based on Hopfield Neural Network with CS

介绍了布谷鸟搜索(cuckoo search,CS)和Hopfield神经网络的基本原理,研究了基于Hopfield神经网络的数字识别应用.针对Hopfield网络权值在数字识别时易陷入局部最优,提出将CS引入Hopfield神经网络的解决方法.利用CS对复杂、多峰、非线性极不可微函数的全局搜索能力,使Hopfield网络在较高噪信比的情况下仍保持较高的联想成功率,并进行了仿真.仿真结果表明,该方法识别数字的效果更佳.

The basic theories of cuckoo search(CS) and Hopfield Neural Network(HNN) are introduced, and the application of Hopfield Network in the digit recognition is researched. Aiming at the problem that Hopfield Neural Network can easily fall into local minimum, a new method that Hopfield network combines CS is presented. The method uses the global search capability of CS for complex, multimodal, nonlinear and non-differentiable functions to make Hopfield network keep a higher success rate even if noise-to-Signal ratio is high, and a simulation was carried out. Experiment results show that this method has a better performance.