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     

用于信息处理的延时混沌神经网络

为了提高混沌神经网络用于信息处理的能力,采用一种参数调节控制方法,通过对一种延时对称全局耦合混沌神经网络的黏合参数的控制研究了网络的动态联想记忆,使被控网络在仅有部分神经元进入周期态的情况下达到输出稳定,并且稳定输出序列只包含与输入模式相关的存储模式及其相反模式。仿真实验说明网络具有良好的容错能力和很高的回忆正确率,适合应用于信息处理和模式识别。     

基于混沌神经网络的故障暂态量选线方法研究

为了有效提取故障暂态信息,研究选取适当的小波包基函数。针对电力系统故障暂态量的特点,为了有效克服非故障暂态信号的干扰,研究选取了容错性和联想记忆功能很强的混沌神经网络实现故障选线,并采用改进的遗传算法对混沌神经网络的权值和参数同时进行训练,加快其收敛速度。根据目标模式与神经元的输出状态构造了数值型选线判据。通过实验算例验证了基于暂态量选线判据的有效性和适用性。     

神经网络的混沌运动与控制

本文采用一种由混沌神经元构成的联想记忆神经网络．以混沌神经网络为基础,研究其非线 性动力学特性、混沌吸引子轨迹以及对初始条件的敏感性, 实现混沌神经网络的动态联想记 忆功能．在网络输入发生较大变异情况下所发生的失忆,本文采用时空系统混沌控制的钉扎 反馈方法,使网络恢复记忆．上述研究通过对异步电动机故障的动态记忆和恢复控制的仿真 实验得到证实．本文研究结果表明,在国内外对神经网络混沌控制研究的热点中,时空系统 的钉扎反馈控制是一种值得推荐的方法;神经网络的混沌控制扩大了网络的容错性,进而提 高了混沌神经网络的实用性,这将在复杂模式识别,图象处理等工程上具有广阔的应用前景 ．     

基于混沌神经网络的故障暂态量选线方法研究

为了有效提取故障暂态信息,研究选取适当的小波包基函数.针对电力系统故障暂态量的特点,为了有效克服非故障暂态信号的干扰,研究选取了容错性和联想记忆功能很强的混沌神经网络实现故障选线,并采用改进的遗传算法对混沌神经网络的权值和参数同时进行训练,加快其收敛速度.根据目标模式与神经元的输出状态构造了数值型选线判据.通过实验算例验证了基于暂态量选线判据的有效性和适用性.     

神经网络的时空混沌控制

当混沌神经网络的输入发生较大变异时,网络混沌运动偏离了原有混沌吸引域,从而丧失了对原有被储存样本模式的记忆.本文针对神经网络的时空特征,采用混沌控制的钉扎反馈方法,使网络重新恢复记忆.通过对应用例的仿真实验表明,对神经网络时空系统的混沌控制,钉扎反馈控制是一种值得推荐的方法;神经网络的混沌控制增强了网络的容错能力及其鲁棒性,进而提高了混沌神经网络的实用性.     

混沌神经网络及其在联想记忆中的应用

提出了一种新型的混沌神经网络模型及其相应的学习算法。该学习算法利用了输入模式各元素之间的关联性信息。此文的神经网络可以用于多值模式的联想记忆,与现有的混沌神经网络相比,具有更好的性能。     

连续学习混沌神经网络的研究

近几年混沌神经网络在信息处理，特别是联想记忆中的应用得到了极大重视。本文提出了一个改进的连续学习混沌神经网络(MSLCNN)模型，它具有两个重要特征：(1)根据不同的输入，神经网络做出不同的响应，可从已知模式来识别未知模式；(2)可连续学习未知模式。计算机仿真表明我们的模型具有应用潜力。     

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。     

基于忆阻器的连续学习混沌神经网络

忆阻器具有独特的记忆功能和连续可变的电导状态,在人工智能与神经网络等研究领域具有巨大的应用优势.详细推导了忆阻器的电荷控制模型,将纳米忆阻器与具有智能信息处理能力的混沌神经网络相结合,提出了一种新型的基于忆阻器的连续学习混沌神经网络模型.利用忆阻器可直接实现网络中繁多的反馈与迭代,即完成外部输入对神经元及神经元之间相互作用的时空总和.提出的忆阻连续学习混沌神经网络可以实现对已知模式和未知模式的区分,并能对未知模式进行自动学习和记忆.给出的计算机仿真验证了方案的可行性.由于忆阻器具有纳米级尺寸和自动的记忆能力,该方案有望大大简化混沌神经网络结构.     

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.     

A transient-chaotic autoassociative network (TCAN) based on Lee oscillators.

In the past few decades, neural networks have been extensively adopted in various applications ranging from simple synaptic memory coding to sophisticated pattern recognition problems such as scene analysis. Moreover, current studies on neuroscience and physiology have reported that in a typical scene segmentation problem our major senses of perception (e.g., vision, olfaction, etc.) are highly involved in temporal (or what we call "transient") nonlinear neural dynamics and oscillations. This paper is an extension of the author's previous work on the dynamic neural model (EGDLM) of memory processing and on composite neural oscillators for scene segmentation. Moreover, it is inspired by the work of Aihara et al. and Wang on chaotic neural oscillators in pattern association. In this paper, the author proposes a new transient chaotic neural oscillator, namely the "Lee oscillator," to provide temporal neural coding and an information processing scheme. To illustrate its capability for memory association, a chaotic autoassociative network, namely the Transient-Chaotic Auto-associative Network (TCAN) was constructed based on the Lee oscillator. Different from classical autoassociators such as the celebrated Hopfield network, which provides a "time-independent" pattern association, the TCAN provides a remarkable progressive memory association scheme [what we call "progressive memory recalling" (PMR)] during the transient chaotic memory association. This is exactly consistent with the latest research in psychiatry and perception psychology on dynamic memory recalling schemes.     

Chaos control and associative memory of a time-delay globally coupled neural network using symmetric map

A chaotic neural network called time-delay globally coupled neural network using symmetric map (TDSG) is proposed for information processing applications. Firstly, its rich dynamic behaviors are exhibited and the output stability is demonstrated by using a parameter modulated control method. Secondly, the associative memory of TDSG is investigated by the control method. It is observed that the stable output sequence only contains stored pattern and its reverse pattern and the TDSG finally converges to the stored pattern which has the smallest Hamming distance to the initial patterns with noise. At last, strong information recovery ability of the TDSG is illustrated by comparative experiments.     

A GCM neural network using cubic logistic map for information processing

A new chaotic neural network described by a modified globally coupled map (GCM) model with cubic logistic map is proposed, which is called CL-GCM model. Its rich dynamical behaviors over a wide range of parameters and the dynamics mechanism of neurons are demonstrated in detail. Furthermore, the network with delay coupling can be precisely controlled to any specified-periodic orbit by feedback control or modulated parameter control with variable threshold. The results of simulations and experiments suggest that the network is controlled successfully. The controlled CL-GCM model exhibits excellent associative memory performance which appears it can output unique fixed pattern or periodic patterns with specified period which contain the stored pattern closest to the initial pattern.

     

基于分组混沌PSO算法的模糊神经网络建模研究

为改善记忆功放建模的精度,且针对粒子群算法早期收敛速度较快,但在后期易陷入早熟收敛,局部最优等特点,提出了一种分组并行混沌粒子群优化算法（Grouping Parallel-Chaotic Particle Swarm Optimization,GP-CPSO）,将分组粒子群优化算法与混沌思想相结合,并用该算法优化动态模糊神经网络（Dynamic Fuzzy Neural Network,DFNN）参数,建立DFNN功放模型。引入分组的CPSO群算法,将种群划分为若干个组,每组单独计算,大大提高了收敛速度,同时将混沌思想运用到每个粒子当中去,避免早熟和局部最优,缩短了迭代时间。通过仿真结果可以看到,GP-CPSO优化后的动态模糊神经网络建模的训练误差减小到0.1以内,收敛速度提高32.5%,从而验证了这种建模方法有效且可靠。     

Large Memory Capacity in Chaotic Artificial Neural Networks: A View of the Anti-Integrable Limit

In the literature, it was reported that the chaotic artificial neural network model with sinusoidal activation functions possesses a large memory capacity as well as a remarkable ability of retrieving the stored patterns, better than the conventional chaotic model with only monotonic activation functions such as sigmoidal functions. This paper, from the viewpoint of the anti-integrable limit, elucidates the mechanism inducing the superiority of the model with periodic activation functions that includes sinusoidal functions. Particularly, by virtue of the anti-integrable limit technique, this paper shows that any finite-dimensional neural network model with periodic activation functions and properly selected parameters has much more abundant chaotic dynamics that truly determine the model's memory capacity and pattern-retrieval ability. To some extent, this paper mathematically and numerically demonstrates that an appropriate choice of the activation functions and control scheme can lead to a large memory capacity and better pattern-retrieval ability of the artificial neural network models.      

带扰动的混沌神经网络的研究

为了研究混沌神经网络的抗扰动能力,在Chen’s混沌神经网络中引入了三角函数扰动项,研究了带扰动的Chen’s混沌神经元模型,给出了该混沌神经元的倒分岔图和Lyapunov指数图,分析了其动力学特性。基于该混沌神经元模型,构造了带扰动的Chen’s混沌神经网络,并将其应用于函数优化和旅行商问题（TSP）。仿真结果表明：Chen’s混沌神经网络有一定的抗扰动能力。