混沌神经网络的同步及其在保密通信中的应用

Synchronization of Chaotic Neural Networks and Its Applications in Secure Communications

利用神经网络的学习、逼近能力构造混沌神经网络 ,提出非线性反馈控制混沌同步方法来同步两个混沌神经网络 ,并基于混沌神经网络的同步给出一种新的混沌保密通信系统 .理论分析和数值实验结果表明 ,新系统具有较强的通用性和柔韧性 ,且有同步速度快。

Synchronization of discrete chaotic systems (i.e. chaotic maps) and its applications in digital secure communications were investigated. These chaotic maps were achieved by designing chaotic neural networks, which are implemented by learning the dynamics of chaotic systems with neural networks. The nonlinear feedback control method was presented to synchronize two identical copies of chaotic neural networks, and a new chaotic secure communication scheme was given based on the synchronization of the networks. The numerical experiments show that the proposed secure communication scheme has advantages of flexible implementation, fast synchronization, high quality recovery information and large amounts of secret key for information encryption.