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研究机载电气故障诊断问题,采用支持向量方法.支持向量机是一种采用结构风险最小化原则的新型机器学习方法,具有出色的学习分类能力和推广能力.支持向量机的参数选择非常重要,决定故障诊断的精确度.针对最小二乘支持向量机的参数通常靠交叉试验来确定的情况,为了提高故障诊断的精度和效率,提出了一种模拟退火遗传算法和最小二乘支持向量机相结合的故障诊断方法,利用模拟退火遗传算法的全局搜索能力对最小二乘支持向量机的参数进行寻优,并以某型直升机机载电气盒的故障诊断为例对方法进行了仿真研究.实验结果表明,模拟退火遗传算法优化的最小二乘支持向量机取得了较好的故障诊断效果. 相似文献
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基于遗传算法和支持向量机的故障诊断方法 总被引:1,自引:1,他引:0
支持向量机是一种采用结构风险最小化原则代替传统统计学中的基于大样本的经验风险最小化原则的新型机器学习方法,具有出色的学习分类能力和推广能力,广泛的应用于故障诊断和函数拟合中;以某型直升机机载电气盒的故障诊断为实验平台,提出了一种自适应遗传算法和最小二乘支持向量机相结合的故障诊断方法,利用自适应遗传算法强大的全局搜索能力对最小二乘支持向量机的参数进行寻优;仿真结果表明,基于自适应遗传算法优化的最小二乘支持向量机取得了较好的故障诊断精度和效率. 相似文献
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一种实用的火电厂飞灰含碳量软测量建模方法 总被引:1,自引:0,他引:1
提出了同时利用自适应加权融合和最小二乘支持向量机建模的实用新方法。首先,给出了基于小波的自适应加权融合和最小二乘支持向量机算法;其次,将BP神经网络、最小二乘支持向量机和基于小波的自适应加权融合的最小二乘支持向量机算法进行建模精度比较;最后,采用真实火电厂飞灰含碳量数据进行模型验证与预测,仿真结果表明基于小波的自适应加权融合的最小二乘支持向量机算法具有较好的建模精度和实用性。 相似文献
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提出一类非线性系统基于最小二乘支持向量机的直接自适应控制方法.该方法采用最小二乘支持向量机构造自适应控制器,自适应控制器参数的在线调整规律由Lyapunov稳定性理论导出,并严格证明了闭环系统的渐近稳定性.仿真研究表明了此控制方案的可行性和有效性. 相似文献
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支持向量机的研究是当前人工智能领域的研究热点。基于支持向量机的大样本回归问题一直是一个非常具有挑战性的课题。最近,基于递归最小二乘算法,Engel等人提出了核递归最小二乘算法。文中基于块增量学习和逆学习过程,提出了自适应迭代回归算法。为了说明两种方法的性能,论文在训练速度、精度和支持向量数量等方面,对它们做了比较。模拟结果表明:核递归最小二乘算法所得到的支持向量个数比自适应迭代回归算法少,而训练时间比自适应迭代回归算法的训练时间长,训练和测试精度也比自适应迭代回归算法差。 相似文献
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一种稀疏最小二乘支持向量分类机 总被引:1,自引:0,他引:1
一般的支持向量分类机需要求解二次规划问题,最小二乘支持向量机只需求解一个线性方程组,但其缺乏稀疏性.为了改进最小二乘支持向量分类机,本文结合中心距离比值及增量学习的思想提出一种基于预选、筛选支持向量的稀疏最小二乘支持向量机.该方法既能弥补最小二乘向量机的稀疏性,减少计算机的存储量和计算量,加快最小二乘支持向量机的训练速度和决策速度,又能对非均衡训练数据造成的分类面的偏移进行纠正,还不影响最小二乘支持向量机的分类能力.3组实验结果也证实了这一点. 相似文献
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针对电力负荷的小样本、非线性、高维数和局部极小点等问题,提出采用最小二乘支持向量机方法建模,以历史负荷、温度、湿度等数据作为输入量,对短期电力负荷进行预测;针对最小二乘支持向量机在建模中存在的参数选取问题,采用一种根据种群多样性信息来指导初始种群选取和避免粒子早熟收敛现象的改进粒子群优化算法来优化最小二乘支持向量机的惩罚因子和核参数。仿真结果表明,基于改进粒子群优化算法和最小二乘支持向量机的短期电力负荷预测方法较最小二乘支持向量机预测方法、基于基本粒子群优化算法和最小二乘向量机的预测方法具有更好的预测精确度。 相似文献
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随着移动互联网技术的快速发展,越来越多的用户通过移动设备获取移动信息和服务,导致信息过载问题日益凸出。针对目前上下文感知推荐算法中存在的数据稀疏性差、上下文信息融入不够、用户相似性度量被忽略等问题,提出一种基于用户聚类和移动上下文的矩阵分解推荐算法。该算法通过利用k-means对用户聚类找到偏好相似的用户簇,求出每簇中并对 用户所处上下文之间的相似度并对其进行排序,由此找出与目标用户偏好和上下文均相似的用户集合,借助该集合改进传统矩阵分解模型损失函数,并以此为基准进行评分预测和推荐。仿真实验结果表明,所提算法可有效提高预测评分的准确度。 相似文献
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随着移动互联网规模的不断扩大,传统推荐系统因较少考虑多种情境因素和用户置信度对用户偏好预测的综合影响,造成了推荐算法预测结果的偏差。针对此问题,将情境信息引入个性化推荐的过程中,提出一种基于情境相似度和二次聚类的协同过滤算法。该算法首先根据用户情境的相似度对用户进行初始聚类,再基于评分矩阵计算用户评分置信度,将用户分为核心用户和非核心用户;然后根据核心用户评分对初始聚类的簇心进行调整,并对簇中非核心用户进行重聚类,形成新的聚簇;最终根据情境相似度对用户偏好进行预测。该算法可以在一定程度上降低评分矩阵中的噪点对聚类结果的影响,提高了推荐结果的准确性。基于实际数据集的仿真实验表明,该算法与传统协同过滤算法相比能够有效提高用户偏好预测的准确性,增加协同过滤推荐算法的精确度。 相似文献
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Tengfei Bao Huanhuan Cao Enhong Chen Jilei Tian Hui Xiong 《Knowledge and Information Systems》2012,31(2):345-370
Mobile context modeling is a process of recognizing and reasoning about contexts and situations in a mobile environment, which
is critical for the success of context-aware mobile services. While there are prior works on mobile context modeling, the
use of unsupervised learning techniques for mobile context modeling is still under-explored. Indeed, unsupervised techniques
have the ability to learn personalized contexts, which are difficult to be predefined. To that end, in this paper, we propose
an unsupervised approach to modeling personalized contexts of mobile users. Along this line, we first segment the raw context
data sequences of mobile users into context sessions where a context session contains a group of adjacent context records which are mutually similar and usually reflect the similar contexts. Then, we exploit two methods for mining personalized
contexts from context sessions. The first method is to cluster context sessions and then to extract the frequent contextual
feature-value pairs from context session clusters as contexts. The second method leverages topic models to learn personalized
contexts in the form of probabilistic distributions of raw context data from the context sessions. Finally, experimental results
on real-world data show that the proposed approach is efficient and effective for mining personalized contexts of mobile users. 相似文献
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Personalization and Context Management 总被引:1,自引:0,他引:1
Andreas Zimmermann Marcus Specht Andreas Lorenz 《User Modeling and User-Adapted Interaction》2005,15(3-4):275-302
Supporting the individual user in his working, learning, or information access is one of the main goals of user modeling.
Personal or group user models make it possible to represent and use information about preferences, knowledge, abilities, emotional
states, and many other characteristics of a user to adapt the user experience and support. Nowadays, the disappearing computer
enables the user to access her information from a variety of personal and public displays and devices. To support a new generation
of contextualized and personalized information and services, this paper addresses the problem of context management. Context management is a new approach to the design of context-aware systems in ubiquitous computing that combines personalization
and contextualization. The presented framework for context management integrates user modeling and context modeling, which
can benefit from each other and give rise to more valid models for personalized and contextualized information delivery. The
paper will introduce a base framework and tools for designing context-aware applications and decompose the underlying framework
into its foundational components. As two illustrative application cases, the paper discusses implementations of an intelligent
advertisement board and an audio-augmented museum environment. 相似文献
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Recommender systems have become indispensable for services in the era of big data. To improve accuracy and satisfaction, context-aware recommender systems (CARSs) attempt to incorporate contextual information into recommendations. Typically, valid and influential contexts are determined in advance by domain experts or feature selection approaches. Most studies have focused on utilizing the unitary context due to the differences between various contexts. Meanwhile, multi-dimensional contexts will aggravate the sparsity problem, which means that the user preference matrix would become extremely sparse. Consequently, there are not enough or even no preferences in most multi-dimensional conditions. In this paper, we propose a novel framework to alleviate the sparsity issue for CARSs, especially when multi-dimensional contextual variables are adopted. Motivated by the intuition that the overall preferences tend to show similarities among specific groups of users and conditions, we first explore to construct one contextual profile for each contextual condition. In order to further identify those user and context subgroups automatically and simultaneously, we apply a co-clustering algorithm. Furthermore, we expand user preferences in a given contextual condition with the identified user and context clusters. Finally, we perform recommendations based on expanded preferences. Extensive experiments demonstrate the effectiveness of the proposed framework. 相似文献
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针对移动机器人未知环境下的趋光控制问题,模拟人或动物“感知-行动”认知机制,对具有趋光特性的移动机器人进行设计,提出一种基于Boltzmann机神经网络的趋光控制方法。该方法首先应用知识集对机器人趋光控制器的Boltzmann机神经网络进行趋光训练;然后应用Boltzmann机神经网络的运行机制实现趋光控制。仿真实验表明,该方法能够提高机器人学习的控制精度。 相似文献
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为了提升基于事件的社交网络(EBSN)中社交事件的推荐效果,提出了融合用户历史行为和社交关系的个性化社交事件推荐方法。首先采用深度学习技术从用户的历史行为以及用户之间的潜在社交关系两个方面建立用户模型;然后在对用户偏好建模时,引入用户偏好的负向量表示,并使用注意力权重层根据不同的候选推荐事件为用户历史行为中不同的事件和用户社交关系中不同的好友分配不同的权重,同时考虑了事件以及群组的多种特征;最后在真实数据集上进行了大量实验。实验结果表明,该个性化社交事件推荐方法在命中率(HR)、归一化折损累计增益(NDCG)、平均倒数排名(MRR)评价指标上优于对比的深度用户社交事件推荐(DUMER)模型和融合注意力机制的深度兴趣网络(DIN)模型。 相似文献
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Moshe Unger Bracha Shapira Lior Rokach Amit Livne 《New Review of Hypermedia and Multimedia》2018,24(3):262-290
ABSTRACTContext-aware systems enable the sensing and analysis of user context in order to provide personalised services. Our study is part of growing research efforts examining how high-dimensional data collected from mobile devices can be utilised to infer users’ dynamic preferences that are learned over time. We suggest novel methods for inferring the category of the item liked in a specific contextual situation, by applying encoder-decoder learners (long short-term memory networks and auto encoders) on mobile sensor data. In these approaches, the encoder-decoder learners reduce the dimensionality of the contextual features to a latent representation which is learned over time. Given new contextual sensor data from a user, the latent patterns discovered from each deep learner is used to predict the liked item’s category in the given context. This can greatly enhance a variety of services, such as mobile online advertising and context-aware recommender systems. We demonstrate our contribution with a point of interest (POI) recommender system in which we label contextual situations with the items’ categories. Empirical results utilising a real world data set of contextual situations derived from mobile phones sensors log show a significant improvement (up to 73% improvement) in prediction accuracy compared with state of the art classification methods. 相似文献
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秦永俊 《计算机测量与控制》2017,25(1):111-113, 118
在移动计算环境下,通过对远程用户的体验数据优化挖掘,满足远程用户的个性化需求,提高对远程用户QoS服务质量;传统的数据挖掘方法采用显著特征关联信息提取算法,当远程用户体验数据之间的差异性特征不明显时,挖掘的准确性不好;提出一种基于关联用户自适应链路跟踪补偿的移动计算环境下远程用户体验数据挖掘模型,进行远程用户体验数据挖掘模型的总体设计和数据结构特征分析,对采集的远程用户体验数据进行非线性时间序列分解,对数据序列通过自相关特征匹配和特征压缩实现挖掘数据的指向性信息优化提取,采用关联用户自适应链路跟踪补偿方法实现对数据挖掘误差的控制和补偿,提高了数据挖掘的准确性和有效性;仿真结果表明,采用该挖掘方法进行移动计算环境下远程用户体验数据挖掘的准确度高,实时性较好,满足了移动远程用户的个性化需求,提高了对用户服务的针对性。 相似文献