神经网络的混沌特性及其在加密通信中的应用

首先概括了能实现混沌动力学特点的主要神经网络模型及其产生混沌同步，混沌序列和混沌吸引子等复杂性的基本原理，介绍了如何利用混沌同步，混沌轨迹序列和混沌吸引子等复杂性特点实现通信加密算法，最后总结有关神经网络的混沌特性及其加密通信应用中需要进一步研究的一些课题。     

混沌神经网络的研究进展

回顾了近年来几种主要混沌神经元模型及混沌神经网络的研究进展,介绍了其特点及主要的应用.已有的研究结果表明,混沌神经网络在求解复杂优化问题和联想记忆等方面比现有网络有着更好的性能.     

混沌神经网络研究进展与展望

概述了混沌动力学的特性，回顾了近年来混沌神经元主混沌神经网络的研究进展，在此基础上，介绍了两种混沌神经网络模型，分析了其构成和特点，已有研究结果表明，混沌神经网络在联想记忆和组合优化等方面有现有网络更好的性能，最后，指出了混沌神经网络的应用与研究方向。     

非线性混沌时序的神经网络预测与控制算法研究

基于神经网络对时序问题的预测能力，本文提出了将混沌和神经网络相结合，应用神经网络来训练混沌序列的预测模型及方法，实现了将混沌系统快速地稳定到期望点上。理论分析和仿真结果均表明了该方法的有效性，且算法弹性大，可扩充性好，稍作修改后，可适应不同的混沌映射。     

一种小波神经网络的混沌加密算法研究

概括了小波神经网络的主要理论,将小波神经网络和混沌系统相结合,建立了一种混沌序列的生成模型,给出基于小波神经网络的混沌加密算法,最后对算法进行计算机仿真实验.结果表明小波神经网络具有更快的收敛速度和更准确的逼近能力,而基于小波神经网络的混沌加密算法具有很高的安全性.     

基于混沌神经网络的发电机定子绕组匝间短路故障诊断

本文采用耦合的混沌振荡子作为单个混沌神经元构造混沌神经网络模型，用改进Hebb算法设计网络的连接权值。在此基础上，实现了混沌神经网络的动态联想记忆并应用该混沌神经网络模型对发电机定子绕组匝间短路故障进行诊断。结果表明，该种方法有助于故障模式的记     

基于混沌理论的再热汽温神经网络模型

在利用混沌理论揭示火电机组再热汽温混沌动力学特性的基础上，构建了再热汽温神经网络预测模型。该模型利用混沌特性处理输入样本并确定神经网络的结构，用神经网络映射混沌相空间的相点演化的非线性关系，采用改进型遗传算法对神经网络模型进行参数辨识。仿真结果表明：该模型精度较高，收敛速度快，为实际生产过程中再热汽温的预测提供了一种新的思路和方法。     

基于GRNN和混沌系统的数字图像加密方法研究

混沌加密技术应用于信息加密是近年信息安全研究的热点问题;首先概括了GRNN神经网络的主要理论,然后将GRNN神经网络和混沌系统相结合建立了一种混沌序列的生成模型,并给出基于GRNN的混沌图像加密算法,该算法只需改变混沌系统的初值,便可获得不同的混沌序列,进而实现对图像数据进行置换加密;最后对算法进行计算机仿真实验;结果表明GRNN神经网络具有更快的收敛速度和更准确的逼近能力,而基于GRNN神经网络的混沌加密算法具有很高的安全性.     

混沌神经网络模型及其应用研究综述

回顾了近年来混沌神经网络模型及其应用的研究进展．首先依据混沌产生的机理，将现有的多种类型混沌神经网络模型归结为4类典型的网络模型，并结合各种网络模型的数学描述来分析各自的机理和特性；然后从复杂问题优化、联想记忆和图像处理、网络与通信、模式识别、电力系统负荷建模和预测5个方面，介绍了混沌神经网络的应用现状；最后评述了混沌神经网络今后的研究方向和研究内容．     

基于动态递归神经网络模型的混沌时间序列预测

提出了一种动态递归神经网络模型进行混沌时间序列预测，以最佳延迟时间为间隔的最小嵌入维数作为递归神经网络的输入维数，并按预测相点步进动态递归的生成训练数据，利用混沌特性处理样本及优化网络结构，用递归神经网络映射混沌相空间相点演化的非线性关系，提高了预测精度和稳定性。将该模型应用于Lorenz系统数据仿真以及沪市股票综合指数预测，其结果与已有网络模型预测的结果相比较，精度有很大提高。因此，证明了该预测模型在实际混沌时间序列预测领域的有效性和实用性。     

小波混沌神经网络模拟退火参数研究

小波混沌神经网络已经成功地解决了函数优化和组合优化问题。研究了分段指数退火函数的Morlet小波混沌神经元模型,给出了分段小波混沌神经元的倒分岔图和Lyapunov指数图。在小波混沌神经网络的基础上,加入了分段指数退火函数,提出了一种新的改进的小波混沌神经网络,并把它应用到函数优化和组合优化问题中。仿真结果表明,改善了小波混沌神经网络的寻优能力,改进的小波混沌神经网络优于原来的小波混沌神经网络。     

Modeling and complexity in neural networks

In this paper, we study nonlinear spatio-temporal dynamics in synchronous and asynchronous chaotic neural networks from the viewpoint of the modeling and complexity of the dynamic brain. First, the possible roles and functions of spatio-temporal neurochaos are considered with a model of synchronous chaotic neural networks composed of a neuron model with a chaotic map. Second, deterministic point-process dynamics with spikes of action potentials is demonstrated with a biologically more plausible model of asynchronous chaotic neural networks. Last, the possibilities of inventing a new brain-type of computing system are discussed on the basis of these models of chaotic neural networks. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998.     

一种基于神经网络混沌序列的对称分组加密方法

根据混沌神经网络的特性如混沌行为特性、对初始条件的敏感性和并行处理特性等等,提出了一种新的对称加密方案;利用混沌神经元的联结矩阵和初始条件作为密钥,使神经网络产生的难以预测的混沌序列来实现非线性数字序列加密运算。从理论分析和仿真结果表明,该系统具有较好的实际保密性,加密效率高,加密速度快,比较适用于数字信号加密通信。     

Exponential Synchronization of Memristive Chaotic Recurrent Neural Networks Via Alternate Output Feedback Control

This paper considers the global exponential synchronization problem of two memristive chaotic recurrent neural networks with time‐varying delays using periodically alternate output feedback control. First, the periodically alternate output feedback control rule is designed for the global exponential synchronization of two memristive chaotic recurrent neural networks. Then, according to the Lyapunov stability theory, we construct an appropriate Lyapunov‐Krasovskii functional to derive several new sufficient conditions guaranteeing exponential synchronization of two memristive chaotic recurrent neural networks under periodically alternate output feedback control. Compared with existing results on synchronization conditions on the basis of linear matrix inequalities of memristive chaotic recurrent neural networks, the derived results complement, extend earlier related results, and are also easy to validate in this paper. An illustrative example is provided to illustrate the effectiveness of the synchronization criteria.     

Memristor-based chaotic neural networks for associative memory

In chaotic neural networks, the rich dynamic behaviors are generated from the contributions of spatio-temporal summation, continuous output function, and refractoriness. However, a large number of spatio-temporal summations in turn make the physical implementation of a chaotic neural network impractical. This paper proposes and investigates a memristor-based chaotic neural network model, which adequately utilizes the memristor with unique memory ability to realize the spatio-temporal summations in a simple way. Furthermore, the associative memory capabilities of the proposed memristor-based chaotic neural network have been demonstrated by conventional methods, including separation of superimposed pattern, many-to-many associations, and successive learning. Thanks to the nanometer scale size and automatic memory ability of the memristors, the proposed scheme is expected to greatly simplify the structure of chaotic neural network and promote the hardware implementation of chaotic neural networks.     

基于混沌映射的纹理图象生成方法

在基于BP神经网络生成纹理图象方法^[1]的基础上，提出了一种基于Logistic映射和多层前向神经网络的纹理图象生成方法，该方法使用Logistic映射来调整多层前向神经网络的网络参数，即用Logistic映射产生一组混沌变量，这组混沌变量中的每一个数对应一个需要调整的神经网络参数，由于Logistic映射具有的混沌特性，使多层前向神经网络每次迭代都会产生一组不同的参数，从而克服了使用BP算法调整神经网络参数时容易收敛的缺点，这种基于混沌映射的方法既保留了基于BP神经网络生成纹理图象方法的优点，又对其进行了改进。该方法因不需要计算网络的误差，从而大大简化了计算过程，并且可以产生比使用原有方法更加丰富的纹理图象，仿真结果表明，使用这种改进后的方法比原有的方法更加简单有效。     

Chaos and rigorous verification of horseshoes in a class of Hopfield neural networks

In this paper, chaos in a new class of three-dimensional continuous time Hopfield neural networks is investigated. Numerical experiments show that this class of Hopfield neural networks can have chaotic attractors and limit cycles for different parameter configurations. By virtue of horseshoes theory in dynamic systems, rigorous computer-assisted verifications are done for their chaotic behavior. In terms of topological entropy, quantitative interpretations of these neural networks’ complexity are given. A brief analysis is also presented about their robustness.     

Lag Synchronization of Unknown Chaotic Delayed Yang–Yang-Type Fuzzy Neural Networks With Noise Perturbation Based on Adaptive Control and Parameter Identification

This paper considers the lag synchronization (LS) issue of unknown coupled chaotic delayed Yang–Yang-type fuzzy neural networks (YYFCNN) with noise perturbation. Separate research work has been published on the stability of fuzzy neural network and LS issue of unknown coupled chaotic neural networks, as well as its application in secure communication. However, there have not been any studies that integrate the two. Motivated by the achievements from both fields, we explored the benefits of integrating fuzzy logic theories into the study of LS problems and applied the findings to secure communication. Based on adaptive feedback control techniques and suitable parameter identification, several sufficient conditions are developed to guarantee the LS of coupled chaotic delayed YYFCNN with or without noise perturbation. The problem studied in this paper is more general in many aspects. Various problems studied extensively in the literature can be treated as special cases of the findings of this paper, such as complete synchronization (CS), effect of fuzzy logic, and noise perturbation. This paper presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed adaptive scheme. This research also demonstrates the effectiveness of application of the proposed adaptive feedback scheme in secure communication by comparing chaotic masking with fuzziness with some previous studies. Chaotic signal with fuzziness is more complex, which makes unmasking more difficult due to the added fuzzy logic.      

Exponential Synchronization of Stochastic Fuzzy Cellular Neural Networks with Reaction-Diffusion Terms via Periodically Intermittent Control

This paper considers the exponential synchronization of stochastic fuzzy cellular neural networks with time-varying delays and reaction-diffusion terms based on p-norm. Motivated by the achievements from both the stability of fuzzy cellular neural networks with stochastic perturbation and reaction-diffusion effects and the synchronization issue of coupled chaotic delayed neural networks by using periodically intermittent control approach, a periodically intermittent controller is proposed to guarantee the exponential synchronization of the coupled chaotic neural networks by using Lyapunov stability theory and stochastic analysis approaches. The synchronization results presented in this paper generalize and improve many known results. This paper also presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed scheme.