Impulsive control for passivity and exponential synchronization of coupled neural networks with multiple weights

This paper investigates the passivity and synchronization problems for two classes of multiple weighted coupled neural networks (MWCNNs) with or without time delays. Firstly, by utilizing an impulsive control strategy and some inequality techniques, several passivity criteria for MWCNNs with diverse dimensions of output and input are established. Then, based on the Lyapunov functional, some sufficient conditions to ensure the synchronization of MWCNNs via impulsive control are derived. In addition, combined with the comparison principle and the impulsive delay differential inequality, the global exponential synchronization of MWCNNs with time-varying delays is considered under impulsive control. Finally, two numerical examples illustrate the effectiveness of the obtained results.     

Applying artificial intelligence technique to predict knowledge hiding behavior

Drawing on psychological ownership and social exchange theories, this study suggests theoretical arguments and empirical evidence for understanding employee reactions to distributive, procedural, and interactional (in)justice — three crucial bases of employees’ feelings of social self-worth. Utilizing field data and artificial intelligence technique, this paper reveals that distributive, procedural, and interactional (in)justice contribute to higher levels of knowledge hiding behavior among employees and that this impact is non-linear (asymmetric). By reuniting the discourses of organizational justice and knowledge management, this study indicates that feelings of psychological ownership of knowledge and the degree of social interaction are mechanisms that work with organizational (in)justice to influence knowledge hiding behavior. The current research may inform contemporary theories of business research and provide normative guidance for managers.     

Negation recognition in medical narrative reports

Substantial medical data, such as discharge summaries and operative reports are stored in electronic textual form. Databases containing free-text clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are such. When searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the documents retrieved will be irrelevant. Hence, negation is a major source of poor precision in medical information retrieval systems. Previous research has shown that negated findings may be difficult to identify if the words implying negations (negation signals) are more than a few words away from them. We present a new pattern learning method for automatic identification of negative context in clinical narratives reports. We compare the new algorithm to previous methods proposed for the same task, and show its advantages: accuracy improvement compared to other machine learning methods, and much faster than manual knowledge engineering techniques with matching accuracy. The new algorithm can be applied also to further context identification and information extraction tasks.

基于NN的随动系统故障诊断

文章首先介绍了人工神经网络的基本原理,其次讨论了反向传播算法,并针对传统BP算法的不足设计了一种基于参数动态改变的BP算法,最后研究了新的算法在某武器随动系统诊断方面的应用.     

基于免疫算法和人工势场的移动机器人路径规划

分析了人工势场模型存在的两个问题:目标不可到达问题和由于局部最优解的存在而产生的死锁问题。在人工势场路径规划的基础上,提出了基于免疫算法和人工势场法的最优路径规划方法。通过仿真算例验证了该模型的有效性。     

“人工智能+教育”的困境根源及应对

以云计算、大数据和深度学习为核心驱动力的人工智能正在如火如荼地开展,它与教育的深度融合(“人工智能+教育”)正在不断变革教育理念、更新教育手段、丰富教育内容。人工智能在教育领域不断推进应用之时,围绕它的批判声也络绎不绝。究其根源而言,人工智能试图驰骋于整个教育疆域但却力不从心,人工智能与教育深度融合过程中出现的新问题使它步履维艰。以此为根,人工智能应用于教育领域而招致的批判具体表现为三个方面:“人工智能+教育”系统、工具的安全性能无法保障,教育数据隐私泄露,教师主导地位沉沦。基于此,可以制定统一的“人工智能+教育”系统、工具开发与设计标准,利用区块链技术保护教育数据隐私,划定人工智能在教育领域的应用边界,从而拨开“人工智能+教育”前行中的重重迷雾。     

面向课堂教学评价的形式化建模与智能计算

随着课堂教学从固化单一的教师传授,向强调小组协作参与的教学转变,如何面向以协作学习为基本特征的课堂实施形成性评价已成为教学评价改革亟需解决的问题。在智能技术支持下,形式化建模可以将复杂多变的课堂教学过程解构,形成数理模型;智能计算可以通过算法评估学生学习状态,并根据教学原则生成教学辅助信息。二者的结合可以促进人类智能与机器智能的有效融合,形成人机协同的课堂评价机制。面向课堂教学评价的形式化建模与智能计算通用架构,自下而上包含教与学行为的感知和存储、教与学行为评估模型的构建、教与学状态的智能计算和教学辅助信息的生成四个部分。前两部分着重对教育情境和问题的表征,是形式化建模的关键步骤;后两部分着重具体技术路线的实现,是智能计算的具体过程和功能体现。整个系统以教与学行为的感知和存储为基础,通过构建评估模型,确定模型的输出;然后引入智能算法对模型进行计算,达成对教与学状态的评估;最后根据相应教学原则,自动生成辅助教师进行课堂教学评价的信息。该通用架构以及人机协同教学、评价机制的进一步完善需要研究者携手教师进行"共同设计",协同教育学、计算机科学、心理学等多学科进行交叉研究。     

教育人工智能伦理问题与伦理原则探讨

文章旨在揭示智能化时代的教育将面临哪些新的伦理问题,并在人工智能伦理和教育伦理的基础上,探讨教育人工智能的伦理原则。运用文献研究和逻辑推理方法,系统梳理了教育人工智能应用的特点和优势,及其可能引发的新的伦理问题:(1)角色层面,系统创建者、教师、学生和监测员等利益相关者将面临更加复杂的伦理困境;(2)技术层面,涉及自动化决策伦理问责、算法伦理和数据伦理等方面的问题;(3)社会层面,可能转变就业市场、改变人际关系和引发新的社会不公平。因此,需要对教育人工智能伦理问题的复杂性有充分的认识,同时需要遵循一般的人工智能伦理原则和教育伦理原则,其中,最核心的内容为二者之间的交集,具体包括:(1)福祉;(2)是非善恶;(3)公平正义;(4)人权和尊严;(5)自由自治;(6)责任和问责。     

不同输精时间对宣凤黄鸡受精率影响的研究

本试验采用人工授精技术 ,对母鸡不同输精时间的研究表明 :9:0 0～ 11:0 0输精受精率为 74 3 % ,11:0 0～13 :0 0输精受精率为 78 5 % ,13:0 0～ 15 :0 0输精受精率为 85 0 % ,15 :0 0～ 17:0 0输精受精率为 95 7% ,17:0 0～ 19:0 0输精受精率为 91 6 % ,19:0 0～ 2 1:0 0输精受精率为 89 2 %。试验结果表明 :每天在 15 :0 0～ 17:0 0输精效果最佳 ,早于 15 :0 0与晚于 17:0 0受精率降低 4 1%以上 ,差异显著 (P <0 0 5 )     

基于神经网络专家系统的供应商信用等级分析

在供应链(SC)中，如何正确地选择敏捷的、有实力和相容的供应商合作伙伴，影响到整个SC的竞争力。而对供应商信用等级进行正确地评价又是实现优化选择供应商合作伙伴的关键，同时也对提升整个SC的竞争力有着极其重要的作用。本文提出了一个供应商信用等级分析指标体系。构建一个基于人工智能神经网络的专家系统，较好地解决对供应商信用等级进行分析评估的问题。