传感器网络中异常数据实时检测算法

如何实时检测传感器网络中异常数据是一项非常重要的工作。通过对线性自回归的分析法,给出传感器数据流的预测模型及其预测机制。当预测出现失败时,给出了一种预测模型自动调整策略,以降低预测误差。基于该预测模型,提出了一种异常数据检测方法,通过计算当前时刻的预测误差与平均预测误差的比值,比较该比值与预先设定的阈值的大小,以检测该时刻数据是否为异常数据。基于该方法,提出了异常事件检测和数据压缩处理的算法。仿真结果验证了预测模型的正确性和有效性,表明该模型能够实时检测异常事件和压缩数据处理。     

自然计算——人工智能的有效实施模式

对自然计算理念给出统一的框架描述,并以反馈式神经网络和群体智能算法为例加以具体论述.在所定义模型框架下,自然计算领域内算法中的相关智能体群表现出一种相对统一的智能计算模式.     

A multiple intelligent agent system for credit risk prediction via an optimization of localized generalization error with diversity

Company bankruptcies cost billions of dollars in losses to banks each year. Thus credit risk prediction is a critical part of a bank＇s loan approval decision process. Traditional financial models for credit risk prediction are no longer adequate for describing today＇s complex relationship between the financial health and potential bankruptcy of a company. In this work, a multiple classifier system （embedded in a multiple intelligent agent system） is proposed to predict the financial health of a company. In our model, each individual agent （classifier） makes a prediction on the likelihood of credit risk based on only partial information of the company. Each of the agents is an expert, but has limited knowledge （represented by features） about the company. The decisions of all agents are combined together to form a final credit risk prediction. Experiments show that our model out-performs other existing methods using the benchmarking Compustat American Corporations dataset.     

A Multiple Intelligence Man-Machine System for Production Management through Hierarchical Cooperation in Mining District

1IntroductionThekeylinksintheproductioninminingdistrictarecoalmininganddrivillg,whichareaffectedbynaturalconditions(suchasgeologicalcondition,occurrenceofcoalseam),technologicalconditions(suchasequipmentsformining),rawmaterialsprovided,marketsforcoal,man-madeandsocialfactors.Thekeysintheproductionmanagementarecompilingproductionplanandoperatingschedule.Becausetherearethedynamicandthestochasticbehaviourintheproductionresultingfi.omthevaritiesofthenatualconditionsandtheexternalsllrroundingsthata…     

基于多级专家系统的矿井设计智能优化方法

针对现有矿井设计优化中直接的数值优化难以直接获得最优结果的问题,本文以缓倾斜煤层为应用条件,提出一种基于多级专家系统的矿井设计智能优化方法,该方法具有启发与联想的智能特点,经实例应用表明,比较好地解决上述长期困扰着矿井设计优化工作者的一大难题。     

电路诊断技术的新发展

本文分析了目前电路诊断技术研究的现状,综合评述了最新的发展方向和趋势。首先论述了诊断理论体系完善的必然性,然后介绍人工智能、模糊数学和人工神经网络技术在电路诊断中的应用,最后分析了基于模型故障诊断的研究情况。     

人工蜂群算法及其应用

为促进人工蜂群算法理论和应用的发展, 在分析人工蜂群算法的基本原理基础上, 针对算法的不足, 全面地归纳了国内外学者对算法的改进研究, 对算法的蜜源初始化、更新策略的改进、调整策略的改进、适应度函数的选择以及与其他算法的融合进行综述, 提出了更有效的改进策略。同时从多方面综述了人工蜂群算法的应用, 并对人工蜂群算法的发展方向进行了总结和展望。     

Improved acetone sensing properties of flat sensors based on Co-SnO2 composite nanofibers

Co-SnO2 composite nanofibers were synthesized by an electrospinning method and characterized by X-ray diffraction, field emission scanning electron microscopy, and transmission electron microscopy. Gas sensors were fabricated by spinning these nanofibers onto flat ceramic substrates, which had signal electrodes and heaters on their top and bottom surfaces, respectively. Compared with sensors loaded with pure SnO2 nanofibers, the Co-SnO2 nanofiber sensors exhibited improved acetone sensing properties with high selectivity and rapid response and recovery times. The response was 33 when the sensors were exposed to 100 μL/L acetone at 330°C, and the corresponding response with 100 μL/L of ethanol was only 6. The response and recovery times to acetone were about 5 and 8 s, respectively. These results indicate Co-SnO2 composite nanofibers are good candidates for fabrication of high performance acetone sensors for practical application.     

如何看待人工智能的发展

人工智能本质上是对人类智能的功能模拟。虽然在某些方面的表现比人类智能更优秀,但是由于其固有的复杂性和技术的局限性,所以充其量只不过是人类智能的延伸,最终在整体上是不能代替人类智能的。     

精度与程度的逻辑差近似算子及其性质

首先研究了程度近似与变精度近似的关系与转化,再利用变精度近似算子与程度近似算子定义了精度与程度的逻辑差近似算子,其具有与精度与程度2个量化指标相关的逻辑含义,最后研究并得到了精度与程度的逻辑差近似算子的一般性质与幂作用性质.精度与程度的逻辑差近似算子部分拓展了程度近似算子与经典近似算子,进而得到了这些已有近似算子幂作用...