Global robust stability of interval delayed neural networks: Modified approach

A criterion for the global robust stability of Hopfield‐type delayed neural networks with the intervalized network parameters is presented. The criterion, which is derived by utilizing the idea of splitting the given interval into two intervals, is in the form of linear matrix inequality and, hence, computationally tractable. The criterion yields a less conservative condition compared with many recently reported criteria, as is demonstrated with an example. Copyright © 2008 John Wiley & Sons, Ltd.     

智能算法求解TSP问题的比较

目前TSP问题的求解方法不仅种类繁多,而且模型迥异。集中讨论求解TSP问题的智能算法,将其分为进化算法、Hopfield神经网络和自组织映射3类,对每类方法进行了原理研究、性能分析和优缺点比较。最后通过不同规模的实验进行验证,发现进化算法与局部搜索的组合求解TSP性能最好。今后的研究将集中在如何寻找更优的局部搜索。     

基于小波混沌神经网络的图像复原算法

提出了一种将小波理论和混沌理论应用于改进的连续Hopfield神经网络的图像复原算法.文章在Hopfield神经网络应用于图像复原基础上,引入小波理论和混沌理论对Hopfield网络进行训练和调整,给出具体的小波混沌神经网络算法方案设计,并将改进后的算法应用于图像复原仿真.实验结果证明该小波混沌神经网络的图像复原方法得到的复原图像信噪比更高,可视效果更佳.     

基于免疫进化规划的多用户检测技术研究

把人工免疫系统和神经网络系统的信息处理机制引入到进化规划算法（EP），提出了免疫进化规划算法．所提IEP通过使用随机Hopfield神经网络制备疫苗构成新的免疫算子，把新的免疫算子结合到进化规划中，不仅加快了进化规划的收敛速度，并提高了进化规划的全局收敛能力．然后在CDMA系统利用此算法设计了新的多用户检测器．仿真结果证明了该方法能够快速收敛到全局最优解，并且无论抗多址干扰和抗远近效应能力都优于传统方法和一些应用优化算法的多用户检测器．     

A Neural Paradigm for Motion Understanding

The main aim of this paper is to propose a new neural algorithm to perform a segmentation of an observed scene in regions corresponding to different moving objects, by analysing a time-varying image sequence. The method consists of a classification step, where the motion of small patches is recovered through an optimisation approach, and a segmen-tation step merging neighbouring patches characterised by the same motion. Classification of motion is performed without optical flow computation. Three-dimensional motion parameter estimates are obtained directly from the spatial and temporal image gradients by minimising an appropriate energy function with a Hopfield-like neural network. Network convergence is accelerated by integrating the quantitative estimation of the motion parameters with a qualitative estimate of dominant motion using the geometric theory of differential equations.     

Medical Data Mining on the Internet: Research on a Cancer Information System

This paper discusses several data mining algorithms and techniques thatwe have developed at the University of Arizona Artificial Intelligence Lab.We have implemented these algorithms and techniques into severalprototypes, one of which focuses on medical information developed incooperation with the National Cancer Institute (NCI) and the University ofIllinois at Urbana-Champaign. We propose an architecture for medicalknowledge information systems that will permit data mining across severalmedical information sources and discuss a suite of data mining tools that weare developing to assist NCI in improving public access to and use of theirexisting vast cancer information collections.     

杂波环境下神经网络数据关联算法

文中提出了一种基于Ｈｏｐｆｉｅｌｄ网络的多目标数据关联算法,构造了多目标数据关联Ｈｏｐｆｉｅｌｄ网络的新能量函数,该函数是观测点迹与航迹预测点迹统计距离的函数．算法考虑了数据关联的整体效果,成功地提高了正确关联概率．仿真表明,在杂波环境下和无杂波环境下该算法较最近邻（ＮＮ）法有明显优势．     

Hopfield神经网络算法求解路网最优路径

为了解决经典算法在求解大规模路网最优路径时运算时间长的问题,研究了Hopfield神经网络的特点,建立了一般路网的数学模型,根据Hopfield神经网络的特点设计了适合车辆诱导的路网Hopfield神经网络最优路径算法.采用动态邻接矩阵对该算法进行了优化,减少了运算时间.把该Hopfield神经网络算法应用于所研发的车辆诱导系统的最优路径求解中,并进行了实际路网测试,结果表明应用该算法能够正确求解路网的最优路径,且比经典算法的运算效率高.     

基于霍普菲尔德神经网络的生猪绿色供应链评价

生猪产业是我国农业的核心产业,有效的生猪绿色供应链绩效评价,有助于保护农业环境、保障猪肉食品安全、提高农产品质量和供应链整体运作。基于绩效评价原则,从生猪绿色供应链业务流程执行水平、运作绩效水平、节点企业绩效水平、绿色水平4个方面为生猪加工配送企业建立了一个三级评价指标体系,并利用离散霍普菲尔神经网络模型对某企业2007-2011年绿色供应链实施评价,研究表明：基于离散霍普菲尔神经网络的生猪绿色供应链评价是合理而有效的。     

四种水质综合评价方法的比较

将人工神经网络技术运用于水质评价,建立了地面水环境质量综合评价的ＢＰ网络模型和Ｈｏｐｆｉｅｌｄ网络模型,并与水质综合评价方法中经典的模糊综合指数法和灰色聚类法进行了比较,结果表明,ＢＰ网络是四种方法中的首选。