三次Hamiltonian对称系统的全局拓扑结构(VI)

本文研究一类三次Hamiltonian对称系统(1),给出了系统所有可能的全局拓扑结构.     

基于三次样条插值计算长江三峡水位与库容关系函数

在研究长江三峡水位与库容的关系基础上,考虑到高次插值函数的计算量大,有剧烈振荡且数值稳定性差(龙格现象),而分段插值在分段点上仅连续而不可导,虽然分段三次Hermite插值有连续一阶导数。鉴于用最优控制理论计算长江三峡经济效益极大值要求状态变量要二阶可导,故根据三次样条函数插值具有一阶、二阶导数收敛性质而提出用三次样条插值方法去计算水位与库容的关系。函数表达式及曲线图证明效果良好。     

浅谈110V变电站内部电气一次设计工作

随着电力市场的不断发展,电气一次设备的性能情况逐渐的被各方的人员所关注,因为做好电气一次设计工作对于整个变电站的安全来讲是非常关键的,本文就以110V变电站作为主要的研究对象,就电气一次设计工作当中的相关要点做一些分析和探讨。     

基于三次样条函数的GPS高程拟合

介绍了三次样条函数的原理和方法,并利用其在较长的带(线)状网内拟合GPS高程,取得了满意的效果,具有一定的科学性和实用性.     

三次Hamiltonian对称系统的全局拓扑结构(Ⅵ)

本文研究一类三次Hamiltonian对称系统(1)，给出了系统所有可能的全局拓扑结构．     

基于MATLAB的RBF神经网络在分类中的应用

RBF神经网络是一种模仿动物神经网络行为特征,进行分布式并行信息处理的算法数学模型,它可以把线性不可分问题转化为线性可分问题,具有逼近任意复杂非线性映射的功能。本文利用RBF神经网络工具箱解决分类方面的问题。     

变风量空调系统的神经网络控制

首先阐述了变风量空调系统及其人工神经网络控制的策略,并从建立模型、样本训练和控制实现方面阐述了BP神经网络在变风量空调系统中的应用,对其不足和改进也做了简单说明。     

基于能量优化的三次B样条曲线拟合

利用三次B样条函数进行曲线拟合,并在能量法的基础上时其进行光顺处理.实例表明,由此得到的拟合曲线在满足约束条件下有较好的整体光顺性.     

浅析人工神经网络

近年来人工神经网络越来越广泛地受到世界各国学者的关注，本文简述了人工神经网络的特点及其应用，重点阐述了人工神经网络的原理。     

人工神经网络

本文介绍了人工神经网络的概念,阐述了神经网络的一般结构和分类,以及神经网络的训练算法及其范化能力的改进,并综述了神经网络的应用。     

基于云计算的三网融合业务平台的设计

分析了三网融合业务平台的演进发展需求,提出了一种基于云计算的业务平台设计思路。     

人工神经网络技术估测镀锌钢板点焊性能的研究

本文介绍了如何将人工神经网络引入到镀锌钢板点焊的质量预测中,通过镀锌钢板点焊工艺试验采集训练样本,建立焊接电流和焊接时间对点焊接头强度的ANN预测网络。验证结果表明,学习后的神经网络预测精度较好,能够合理地预测焊接电流和焊接时间对镀锌钢板点焊接头强度的影响规律。     

遗传物质—信息系统—先天三元

现代行为遗传学认为,人的精神亦即信息具有遗传效应,这与元极学提出的“人的信息系统以先天三元形式共存于人的物质系统”颇相一致。     

三网融合技术中一种高速流媒体服务器平台的设计

高速流媒体服务器是三网融合中流媒体服务的关键设备。本文介绍一种高速流媒体服务器的网络结构、系统架构到关键技术的设计方案,该方案解决了海量存储并发、实时动态编解码技术等,为用户搭建系统提供了一种可靠的选择。     

Negative Can Be Positive: Signed Graph Neural Networks for Recommendation

Most of the existing GNN-based recommender system models focus on learning users’ personalized preferences from these (explicit/implicit) positive feedback to achieve personalized recommendations. However, in the real-world recommender system, the users’ feedback behavior also includes negative feedback behavior (e.g., click dislike button), which also reflects users’ personalized preferences. How to utilize negative feedback is a challenging research problem. In this paper, we first qualitatively and quantitatively analyze the three kinds of negative feedback that widely existed in real-world recommender systems and investigate the role of negative feedback in recommender systems. We found that it is different from what we expected — not all negative items are ranked low, and some negative items are even ranked high in the overall items. Then, we propose a novel Signed Graph Neural Network Recommendation model (SiGRec) to encode the users’ negative feedback behavior. Our SiGRec can learn positive and negative embeddings of users and items via positive and negative graph neural network encoders, respectively. Besides, we also define a new Sign Cosine (SiC) loss function to adaptively mine the information of negative feedback for different types of negative feedback. Extensive experiments on four datasets demonstrate the proposed model outperforms several existing models. Specifically, on the Zhihu dataset, SiGRec outperforms the unsigned GNN model (i.e., LightGCN), 27.58% 29.81%, and 31.21% in P@20, R@20, and nDCG@20, respectively. We hope our work can open the door to further exploring the negative feedback in recommendations.     

Filtering search results using an optimal set of terms identified by an artificial neural network

Information filtering (IF) systems usually filter data items by correlating a set of terms representing the user’s interest (a user profile) with similar sets of terms representing the data items. Many techniques can be employed for constructing user profiles automatically, but they usually yield large sets of term. Various dimensionality-reduction techniques can be applied in order to reduce the number of terms in a user profile. We describe a new terms selection technique including a dimensionality-reduction mechanism which is based on the analysis of a trained artificial neural network (ANN) model. Its novel feature is the identification of an optimal set of terms that can classify correctly data items that are relevant to a user. The proposed technique was compared with the classical Rocchio algorithm. We found that when using all the distinct terms in the training set to train an ANN, the Rocchio algorithm outperforms the ANN based filtering system, but after applying the new dimensionality-reduction technique, leaving only an optimal set of terms, the improved ANN technique outperformed both the original ANN and the Rocchio algorithm.     

Adaptive output feedback neural network control of uncertain non-affine systems with unknown control direction

This paper deals with the problem of adaptive output feedback neural network controller design for a SISO non-affine nonlinear system. Since in practice all system states are not available in output measurement, an observer is designed to estimate these states. In comparison with the existing approaches, the current method does not require any information about the sign of control gain. In order to handle the unknown sign of the control direction, the Nussbaum-type function is utilized. In order to approximate the unknown nonlinear function, neural network is firstly exploited, and then to compensate the approximation error and external disturbance a robustifying term is employed. The proposed controller is designed based on strict-positive-real (SPR) Lyapunov stability theory to ensure the asymptotic stability of the closed-loop system. Finally, two simulation studies are presented to demonstrate the effectiveness of the developed scheme.     

Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach

The advancement in mobile technology has enabled the application of the mobile wallet or m-wallet as an innovative payment method to substitute the traditional functions of the physical wallet. However, because of pro-innovation bias, scholars have a focus on the adoption of technology and very little attention has been given to the resistance of innovation, especially in the m-wallet context. This study addressed this absence by examining the inhibitors of m-wallet innovation adoption through the lens of innovation resistance theory (IRT). By applying a sophisticated two-staged structural equation modeling-artificial neural network (SEM-ANN) approach, we successfully extended the IRT by integrating socio-demographics and perceived novelty. The study has unveiled the noncompensatory and nonlinear relationships between the predictors and m-wallet resistance. Significant predictors from SEM analysis were taken as the ANN model’s input neurons. According to the normalized importance obtained from the multilayer perceptrons of the feed-forward-back-propagation ANN algorithm, we found significant effects of education, income, usage barrier, risk barrier, value barrier, tradition barrier, and perceived novelty on m-wallet innovation resistance. The ANN model can predict m-wallet innovation resistance with an accuracy of 76.4 %. We also discussed several new and useful theoretical and practical implications for reducing m-wallet innovation resistance among consumers.