遗传算法和Hopfield模型求解货郎担问题的比较和分析

文章简要介绍了运用遗传算法和Hopfield网络求解货郎担问题的模型,讨论了两种算法中有代表性的实现途径,并给出了两种方法的具体算法。文中根据实验数据,着重对两种算法的性能进行了比较和分析。     

癌细胞显微图像分割与识别研究

文章采用竞争Hopfield神经网络的自动聚类分割方法从腹水脱落细胞显微图像中分割可疑细胞和可疑细胞核,提取癌细胞的15个形态特征,利用柔性BP神经网络分类器对腹水脱落癌细胞进行分类识别。通过对临床病例的检验分析,表明该方法能获得较高的诊断正确率。     

基于任意给定训练集的离散型Hopfield网学习算法

本文提出了一个离散型Hopfield网联想记忆学习算法,该算法增加了训练样本的维数,因而能存储任意给定的训练模式集.实验结果也证明了该方法的有效性.     

基于Hopfield神经网络的交叉线识别算法

提出了一种利用Ｈｏｐｆｉｅｌｄ神经网络进行交叉线识别的算法。在本算法中，一个含有交叉线的图象被映射到Ｈｏｐｆｉｅｌｄ神经网络之上，每一个象素与一个神经元相对应，神经元间的权重由其对应的象素间的关系（包括距离、斜率等）所决定。在Ｈｏｐｆｉｅｌｄ神经网络的收敛过程中，神经元的状态根据一定的规则不断地被调整直到收敛；同时随着神经元状态的确定，交叉线段也被识别出来。在实验模拟中，此算法显示了优越的性能。     

一类具有时滞的广义Hopf ield 神经网络的全局稳定性

通过引入能量泛函，分析了一类具有时滞的广义Hopfield神经网络的全局稳定性．从理论上给出了该类网络为全局稳定的充分条件，证明了当时滞满足一个可计算的边界条件时，具有时滞的该类神经网络与相应的无时滞网络具有同样的全局稳定特性．仿真结果进一步证明了结论的有效性。     

Fault diagnosis using partial syndromes: a modified Hopfield neural network approach

This paper presents a modified Hopfield neural network (HNN) for solving the system-level fault diagnosis problem which aims at identifying the set of faulty nodes. This problem has been extensively studied in the last three decades. Nevertheless, identifying the set of all faulty nodes using only partial syndromes, i.e. when some of the testing or comparison outcomes are missing prior to initiating the diagnosis phase, remains an outstanding research issue. The new HNN-based diagnosis algorithm does not require any prior learning or knowledge about the system, nor about any faulty situation, hence providing a better generalisation performance. Results from a thorough simulation study demonstrate the effectiveness of the HNN-based fault diagnosis algorithm in terms of diagnosis correctness, diagnosis latency and diagnosis scalability, for randomly generated diagnosable systems of different sizes and under various fault scenarios. We have also conducted extensive simulations using partial syndromes. Simulations showed that the HNN-based diagnosis performed efficiently, i.e. diagnosis correctness was around 99% when at most half of the test or comparison outcomes are missing, making it a viable alternative to existing diagnosis algorithms.     

Agriculture satellite image segmentation using a modified artificial Hopfield neural network

Beekeeping plays an important role in increasing and diversifying the incomes of many rural communities in Kingdom of Saudi Arabia. However, despite the region’s relatively good rainfall, which results in better forage conditions, bees and beekeepers are greatly affected by seasonal shortages of bee forage. Because of these shortages, beekeepers must continually move their colonies in search of better forage. The aim of this paper is to determine the actual bee forage areas with specific characteristics like population density, ecological distribution, flowering phenology based on color satellite image segmentation. Satellite images are currently used as an efficient tool for agricultural management and monitoring. It is also one of the most difficult image segmentation problems due to factors like environmental conditions, poor resolution and poor illumination. Pixel clustering is a popular way of determining the homogeneous image regions, corresponding to the different land cover types, based on their spectral properties. In this paper Hopfield neural network (HNN) is introduced as Pixel clustering based segmentation method for agriculture satellite images.     

反馈电压偏置型Hopfield 网络电域盲检测相干光通信信号

为解决无线分集相干光接收机的自适应盲检测问题,提出了一种新的离散时间连续状态的网络输出反馈偏置型的复Hopfield 神经网络用以解决多值QAM 信号的盲检测问题。反馈电压偏置的引入即不脱离传统Hopfield 模型,又能有效满足多值信号检测时所需的搜索空间变大的特殊要求。全文完成多值信号盲检测的优化问题构造和能量函数的映射,给出能量函数的证明、分析和它的约束条件,给出适用该问题的激活函数的基本特征,正确盲检测信号的权矩阵的配置方法。最后,通过详细的仿真结果展示和与其他算法性能对比进一步验证算法的有效性和优越性并指出算法所存在的问题和下一步的研究方向。     

物流中心选址算法改进及其Hopfield神经网络设计

在分析物流中心选址传统算法的基础上,引入一种新的选址模型,该模型能减少决策变量和约束条件的个数.利用该模型设计了一种Hopfield神经网络,将约束合并进网络结构从而将罚函数从能量函数中消除,使得网络的运行时间显著降低.为物流中心选址优化提供了一种新的方法.     

On asymptotic stability of discrete-time non-autonomous delayed Hopfield neural networks

In this paper, we obtain some sufficient conditions for determining the asymptotic stability of discrete-time non-autonomous delayed Hopfield neural networks by utilizing the Lyapunov functional method. An example is given to show the validity of the results.