混沌神经网络逆控制的同步及其在保密通信系统中的应用

Synchronization of chaotic neural networks based on adaptive inverse control 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,提出逆控制混沌同步方法来同步两个混沌神经网络,并基于逆控制和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,新系统能够有效地克服信道噪声对信息传输的不良影响,具有较强通用性和柔韧性,且有同步速度快,信号恢复精度高和密钥量大的优点.

Based on adaptive inverse control theory, a new chaotic neural networks synchronous system and its secure communication are designed. The synchronization of chaotic neural networks system in the presence of perturbation is studied. The proposed method can overcome effectively the noise pollution of channel in transmitting information. The numerical experiments show that the proposed secure communication scheme has advantages of flexible implementation, fast synchronization, high-quality recovery of information and large amounts of secret key for information encryption.