进化策略求解Hopfield神经网络

提出了一种进化策略求解ＨＯｐｆｉｅｌｄ神经网络的方法。该进化策略分三个阶段,即第一阶段只在较小区间上求出局部优化解;然后,在此基础上,由第二阶段求出较大区间上的局部优化解;最后由第三阶段求出全局优化解。同时采用Ｈｏｐｆｉｅｌｄ神经网络动态方程指导第一阶段的局部进化策略的进化方向,因而大大加快了优化搜索速度。在分阶段的进化策略中,其第一阶段只需搜索较小区间、第二和第三阶段的搜索则建立在其前一阶段的基     

利用多种特征和Hopfield神经网络的有遮挡的目标识别

该文提出了一种新的识别有遮挡目标的方法,即将目标模型和含有目标的遮挡图象的轮廊在某一尺度上张角的极值、极值点（亦称显著点）之间距离、相对位置等信息集成在一起,作为描述目标模型（遮挡图象）的一组特征,且这组特征在平移、旋转和均匀尺度变换下保持不变。其轮廓上点ｐ处的张角可用余弦定理很方便地求出,而张角的极值点则对应于轮廊急剧变化的地方。同时将特征匹配定义为模型特征与遮挡图象特征之间的对应,若这种对应被映射到Ｈｏｐｆｉｅｌｄ神经网络上,则该网络即可用于完成全局特征匹配。该文提出了的方法已在ＰⅡ个人计算机上用Ｍａｔｌａｂ５．２编程实现,并给出了应用实例。实验结果表明,该方法能有效地从含有遮挡目标的景物图象中识别出目标,且实现起来非常方便。     

Associative Memories in Infinite Dimensional Spaces

A generalization of the Little–Hopfield neural network model for associative memories is presented that considers the case of a continuum of processing units. The state space corresponds to an infinite dimensional euclidean space. A dynamics is proposed that minimizes an energy functional that is a natural extension of the discrete case. The case in which the synaptic weight operator is defined through the autocorrelation rule (Hebb rule) with orthogonal memories is analyzed. We also consider the case of memories that are not orthogonal. Finally, we discuss the generalization of the non deterministic, finite temperature dynamics.     

RNN和Hopfield两网络中的优化解法的对比研究

基于一种动态随机神经网络(DRNN)求解典型NP优化问题TSP的改进算法，在理论上对DRNN与连续的Hopfiled网络(CHNN)进行了对比研究，指出虽然两种网络均以能量函数表达TSP的最优路径，并通过训练反馈网络求得路径解，但由于两者所用激活函数和收敛条件不同，使得DRNN网络能够接受能量函数的小波动，从而跳出局部最小值达到全局最优；此外，DRNN与CHNN相比网络训练对参数变化不敏感，参数设置简单。最后，通过仿真实验对随机坐标十城市使用两种网络对比路径寻优能力，进一步验证理论分析的结论。揭示RNN网络和CHNN网络在求解TSP时各自的优缺点。     

基于离散Hopfield网络求解极大独立集的茎区选择算法以及在RNA二级结构预测中的应用

提出了一种利用离散Hopfield网络求解图论极大独立集的启发式算法,并将其应用于RNA二级结构的茎区选择和预测当中.算法通过映射RNA序列的茎区为无向图中的节点,将预测RNA二级结构的问题转化为求解图的极大独立集的问题.定义了合理的能量变化函数,利用离散Hopfield网络进行迭代,以获得能量最优的预测结构.文中将算法与传统的最大匹配数算法以及最小自由能算法在运行时间上进行比较,并且选择特定的序列在茎区和碱基对水平上进行精度测试,结果证明该算法在效率和精度上具有一定的优势.算法的时间复杂性为max{O(n2),O(N2)},空间复杂度为O(N2),其中n为RNA序列长度,N为RNA的茎区段个数.     

MC-CDMA系统的神经鱼群算法检测器

为使人工鱼群算法在最短的时间内取得多用户检测问题的最优解,在MC-CDMA系统基础上设计一种神经网络人工鱼群算法。人工鱼的3种神经网络行为使神经网络人工鱼群算法在解决多用户检测该类组合优化问题时,减少搜索的随机性和任意性,加快原鱼群算法的收敛速度。仿真结果证明,该算法能够快速收敛,且其抗多址干扰能力和抗远近效应能力优于已有应用智能算法的多用户检测器。     

基于离散Hopfield神经网络的噪声数字识别

在Hebb学习规则的基础上,运用离散Hopficld神经网络的联想记忆能力,对含有噪声而产生畸变的0~9数字进行了识别。通过改进神经网络的记忆样本,即先对记忆样本做正交化处理,再对改进后的记忆样本进行学习,得到相应的权值矩阵,然后利用改进后的离散Hopfield神经网络根据待识别噪声数字的信息联想已记忆的数字。实验结果表明,改进后的神经网络对噪声数字有较好的识别效果,提高了记忆能力和识别的正确率。     

Noise removal using hysteretic Hopfield tunnelling network in message transmission systems

A novel approach of two-tier noise removal scheme for message transmission systems is proposed with hysteretic Hopfield tunnelling network (HHTN). The proposed system increases the security of messages and reduces the complexity of recognition of characters due to external distortion or diffusion. Though there are many error detection codes, these codes request retransmission when there is an error. If the error rate is high the number of retransmissions is high which causes a delay in data communication. Kim et al. [Y.S. Kim, Y.M. Kim, J.Y. Choi, and D.K. Baik, Information hiding system stegowaveK for improving capacity, Parallel Distributed Comput. (2003), pp. 423–426] proposed an alternate two-tier communication system with code division multiple access (CDMA) technology and a mapping table. When HHTN is added to the system, the learning ability enables the network to remember patterns and enhances the speed. But when images of larger size are stored, the network fails to recognize, which leads to further research in this area.     

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。     

Improved watershed transform for tumor segmentation: Application to mammogram image compression

In this study, an automatic image segmentation method is proposed for the tumor segmentation from mammogram images by means of improved watershed transform using prior information. The segmented results of individual regions are then applied to perform a loss and lossless compression for the storage efficiency according to the importance of region data. These are mainly performed in two procedures, including region segmentation and region compression. In the first procedure, the canny edge detector is used to detect the edge between the background and breast. An improved watershed transform based on intrinsic prior information is then adopted to extract tumor boundary. Finally, the mammograms are segmented into tumor, breast without tumor and background. In the second procedure, vector quantization (VQ) with competitive Hopfield neural network (CHNN) is applied on the three regions with different compression rates according to the importance of region data so as to simultaneously reserve important tumor features and reduce the size of mammograms for storage efficiency. Experimental results show that the proposed method gives promising results in the compression applications.