摘要
建立了径向基函数混沌神经网络模型以及径向基函数混沌神经元模型,分析其产生混沌后收敛的原因,通过撤销模拟退火策略使过程无法收敛,从而构建出永久保持混沌状态的混沌神经元动力系统,分析了该系统的时间序列指标,证明其永久保持混沌状态的可行性;将该系统应用于灰度图像的加密解密,阐述了其原理及算法;分析了该算法的抗穷举能力,考察了原图像与加密图像的直方图,由此说明了该算法的抗统计分析的能力。
Construct the Radial Basis Function（RBF）chaotic neural network model and the RBF chaotic neuron model. Analyze the reason for convergence after the chaotic search. Construct the RBF chaotic neuron dynamic system which can keep chaotic state forever through removing the simulated annealing strategy. Prove the feasibility of keeping chaotic state forever from analyzing the time series of this system. Apply this system to encrypt and decipher for the gray image. Illuminate the principle and the algorithm for this application. Analyze the capability of resisting exhaustion and explain the capability of resisting statistic through checking the histogram of the original image and the encrypted image.
出处
《计算机工程与应用》
CSCD
2014年第4期73-76,102,共5页
Computer Engineering and Applications
关键词
径向基函数
混沌
模拟退火策略
时间序列
Radial Basis Function（RBF）
chaos
simulated annealing strategy
time series
