检索结果-内蒙古大学图书馆

20th Chinese Intelligent Systems Conference

作者： Fu, Yihao Mo, Lipo Beijing Technol & Business Univ Sch Math & Stat Beijing 100048 Peoples R China

ISBN: (纸本)9789819786565;9789819786541;9789819786534

This paper explores online noncooperative games (NGs) with constrained involving multi-agent systems on unbalanced directed graphs (digraphs), where players try to minimize their objective functions selfishly, and the objective functions and decisions of it are vary with time. Additionally, the players are bound to time-varying constraints. To seek the stable sequence of the game online, that is the generalized Nash equilibrium (GNE) sequence, we developed a distributed online learning algorithm, which utilizing primal-dual, gradient descent, and projection methods. This approach achieved sublinear bounded dynamic regrets and constraint violations. Ultimately, the example of online electricity market games demonstrates the effectiveness of the introduced algorithm.

关键词： Generalized nash equilibrium Multi-agent systems online learning algorithm Noncooperative games Unbalanced digraphs

来源：评论

学校读者我要写书评

暂无评论

online learning algorithm of Gaussian Process Based on Adaptive Natural Gradient for Regression

Online Learning Algorithm of Gaussian Process Based on Adapt...

引用

International Conference on Manufacturing Engineering and Automation

作者： Shen, Qianqian Sun, Zonghai S China Univ Technol Coll Automat Sci & Engn Guangzhou 510640 Peoples R China

ISBN: (纸本)9780878492268

dGaussian Process (GP) is a new learning method on nonlinear system modeling. The most common way of model training is conjugate gradient method, but this method should compute Heisenberg matrix which needs much computing resource. It is not a suitable training method for online learning algorithm. There is one online learning algorithm of GP which is named sparse online GP now. This algorithm has constraint to the training data sets. In order to satisfy the real-time modeling without the limit of the training data sets, an online algorithm of GP based on adaptive natural gradient (ANG) is proposed in this paper. The algorithm is applied in Continuous Stirred Tank Reactor (CSTR) modeling and the sparse online GP is also applied in CSTR modeling for comparison. Obtained from the simulation results, the algorithm is effective and has higher Accuracy compared with the sparse online GP algorithm.

关键词： Gaussian process regression online learning algorithm Adaptive nature gradient CSTR modeling

来源：评论

学校读者我要写书评

暂无评论

Vibration-based damage detection using online learning algorithm for output-only structural health monitoring

引用

STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2018年第4期17卷 727-746页

作者： Jin, Seung-Seop Jung, Hyung-Jo Korea Adv Inst Sci & Technol Dept Civil & Environm Engn 291 Daehak Ro Daejeon 305701 South Korea

Damage-sensitive features such as natural frequencies are widely used for structural health monitoring;however, they are also influenced by the environmental condition. To address the environmental effect, principal component analysis is widely used. Before performing principal component analysis, the training data should be defined for the normal condition (baseline model) under environmental variability. It is worth noting that the natural change of the normal condition may exist due to an intrinsic behavior of the structural system. Without accounting for the natural change of the normal condition, numerous false alarms occur. However, the natural change of the normal condition cannot be known in advance. Although the description of the normal condition has a significant influence on the monitoring performance, it has received much less attention. To capture the natural change of the normal condition and detect the damage simultaneously, an adaptive statistical process monitoring using online learning algorithm is proposed for output-only structural health monitoring. The novelty aspect of the proposed method is the adaptive learning capability by moving the window of the recent samples (from normal condition) to update the baseline model. In this way, the baseline model can reflect the natural change of the normal condition in environmental variability. To handle both change rate of the normal condition and non-linear dependency of the damage-sensitive features, a variable moving window strategy is also proposed. The variable moving window strategy is the block-wise linearization method using k-means clustering based on Linde-Buzo-Gray algorithm and Bayesian information criterion. The proposed method and two existing methods (static linear principal component analysis and incremental linear principal component analysis) were applied to a full-scale bridge structure, which was artificially damaged at the end of the long-term monitoring. Among the three methods, t

关键词： Structural health monitoring environmental variability adaptive statistical process monitoring online learning algorithm variable moving window strategy

来源：评论

学校读者我要写书评

暂无评论

Automated human emotion recognition using hybrid approach based on sensitivity analysis on residual time-frequency plane with online learning algorithm

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第1期84卷

作者： Sharma, Rahul Teerthanker Mahaveer Univ Moradabad Dept Elect & Commun Engn Moradabad 244001 Uttar Pradesh India

Emotions are neurophysiological changes that are modulated by central , peripheral nerve modulators that cause transient agitation due to the synchronized firing of nerve cells. Hence, the electroencephalogram (EEG) signal perfectly reflected the stimulated excitations of emotions. The excitation of these neurons corresponding to different emotions varies with instantaneous time. The focus of this work is to develop effective, efficient , scalable data mining tools to study brain signals. The sensitivity analysis technique is employed to extract the valuable knowledge embedded in the vast amounts of brain data. It measures dilation parameters as a function of spectral domain changes without compromising the integrity of the original data. A residual time-frequency wavelet analogy is introduced to explore the EEG signals in the high-dimensional plane. Various nonlinear statistics are measured to extract the underlying complexity and variance discrepancy from the residual time-frequency domain. The feature matrix is further processed by a feature reduction and selection algorithm followed by a classifier to maximize the classification probability. This emotionally labeled feature matrix is augmented to enhance the dimension of the feature space. A bi-LSTM based deep learning network is used to secure a maximum probability of classification in a short time. The results reveal that the proposed algorithm maintains multiple potentials to achieve an excellent classification accuracy of order 78.37%, which helps researchers to understand the EEG patterns associated with each emotion.

关键词： Electroencephalogram signal Spectral domain Residual wavelet transform Statistical variants online learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks

引用

PATTERN RECOGNITION LETTERS 2011年第14期32卷 1930-1935页

作者： Hieu Trung Huynh Won, Yonggwan HoChiMinh City Univ Ind Fac Informat Technol Ho Chi Minh City Vietnam Chonnam Natl Univ Dept Comp Engn Kwangju 500757 South Korea Chonnam Natl Univ Korea Bio IT Foundry Ctr Gwangju Kwangju 500757 South Korea Nguyen Tat Thanh Univ NTT Inst Hitechnol Ho Chi Minh City Vietnam

online learning algorithms have been preferred in many applications due to their ability to learn by the sequentially arriving data. One of the effective algorithms recently proposed for training single hidden-layer feedforward neural networks (SLFNs) is online sequential extreme learning machine (OS-ELM), which can learn data one-by-one or chunk-by-chunk at fixed or varying sizes. It is based on the ideas of extreme learning machine (ELM), in which the input weights and hidden layer biases are randomly chosen and then the output weights are determined by the pseudo-inverse operation. The learning speed of this algorithm is extremely high. However, it is not good to yield generalization models for noisy data and is difficult to initialize parameters in order to avoid singular and ill-posed problems. In this paper, we propose an improvement of OS-ELM based on the bi-objective optimization approach. It tries to minimize the empirical error and obtain small norm of network weight vector. Singular and ill-posed problems can be overcome by using the Tikhonov regularization. This approach is also able to learn data one-by-one or chunk-by-chunk. Experimental results show the better generalization performance of the proposed approach on benchmark datasets. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Neural networks online learning algorithm ELM OS-ELM ReOS-ELM Multiobjective training algorithms

来源：评论

学校读者我要写书评

暂无评论

An Improved online learning algorithm and its Applications on Leak Points Prediction of Gas Pipe in Petrochemical Industries

An Improved Online Learning Algorithm and its Applications o...

引用

Annual Conference of the IEEE Industrial Electronics Society

作者： Linchao Zhuo Kun Wang Lei Shu Chunsheng Zhu Zhiyou Ouyang Nanjing University of Posts and Telecommu University of California Guangdong University of Petrochemical Technology University of British Columbia

ISBN: (纸本)9781479940318

In petrochemical industries, one of the most concerned problems is the leaking of toxic gas. Once leaking occurs, the safety of equipments located in production site is greatly threatened, thereby affecting surrounding environment. In order to solve this problem, it is necessary to predict the possible location of leak points from sensors which are located in gas pipe. On the other hand, data from sensors of petrochemical industries need to be timely operated because of time sensitivity, and it is hard to achieve associated information from sensors located in production site. To this end, an OLA-IBP (online learning algorithm based on Improved Back Propagation) is proposed. The adaptive structure of this algorithm is settled on-line. Meanwhile, real-time data streams are parallelly processed according to arriving time in input layer. Simulation results show that OLA-IBP can efficiently improve learning time and accuracy rate. Finally, the adaptability of OLA-IBP is verified in leak points prediction of petrochemical equipments from processed data.

关键词： BP algorithm online learning algorithm Leak points prediction Petrochemical industries

来源：评论

学校读者我要写书评

暂无评论

online learning of order flow and market impact with Bayesian change-point detection methods

引用

QUANTITATIVE FINANCE 2025年第2期25卷 307-322页

作者： Tsaknaki, Ioanna-Yvonni Lillo, Fabrizio Mazzarisi, Piero Scuola Normale Super Pisa Piazza Cavalieri 7 I-56126 Pisa Italy Univ Bologna Dept Math Piazza Porta San Donato 5 I-40126 Bologna Italy Univ Siena Dept Econ & Stat Banchi Sotto 55 I-53100 Siena Italy

Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose the use of Bayesian online change-point detection (BOCPD) methods to identify regime shifts in real-time and enable online predictions of order flow and market impact. To enhance the effectiveness of our approach, we have developed a novel BOCPD method using a score-driven approach. This method accommodates temporal correlations and time-varying parameters within each regime. Through empirical application to NASDAQ data, we have found that: (i) Our newly proposed model demonstrates superior out-of-sample predictive performance compared to existing models that assume i.i.d. behavior within each regime;(ii) When examining the residuals, our model demonstrates good specification in terms of both distributional assumptions and temporal correlations;(iii) Within a given regime, the price dynamics exhibit a concave relationship with respect to time and volume, mirroring the characteristics of actual large orders;(iv) By incorporating regime information, our model produces more accurate online predictions of order flow and market impact compared to models that do not consider regimes.

关键词： Regime switching models Bayesian methods Price impact Metaorders online learning algorithm

来源：评论

学校读者我要写书评

暂无评论

online learning algorithms for Train Automatic Stop Control Using Precise Location Data of Balises

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2013年第3期14卷 1526-1535页

作者： Chen, Dewang Chen, Rong Li, Yidong Tang, Tao Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

For urban metro systems with platform screen doors, train automatic stop control (TASC) has recently attracted significant attention from both industry and academia. Existing solutions to TASC are challenged by uncertain stopping errors and the fast decrease in service life of braking systems. In this paper, we try to solve the TASC problem using a new machine learning technique and propose a novel online learning control strategy with the help of the precise location data of balises installed at stations. By modeling and analysis, we find that the learning-based TASC is a challenging problem, having characteristics of small sample sizes and online learning. We then propose three algorithms for TASC by referring to heuristics, gradient descent, and reinforcement learning (RL), which are called heuristic online learning algorithm (HOA), gradient-descent-based online learning algorithm (GOA), and RL-based online learning algorithm (RLA), respectively. We also perform an extensive comparison study on a real-world data set collected in the Beijing subway. Our experimental results show that our approaches control all stopping errors in the range of +/- 0.30 m under various disturbances. In addition, our approaches can greatly increase the service life of braking systems by only changing the deceleration rate a few times, which is similar to experienced drivers. Among the three algorithms, RLA achieves the best results, and GOA is a little better than HOA. As online learning algorithms can dynamically reduce stopping errors by using the precise location data from balises, it is a promising technique in solving real-world problems.

关键词： Balise online learning algorithm reinforcement learning (RL) train automatic stop control (TASC) urban metro systems

来源：评论

学校读者我要写书评

暂无评论

online DDoS attack detection using Mahalanobis distance and Kernel-based learning algorithm

引用

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2020年 168卷 102756-102756页

作者： Cakmakci, Salva Daneshgadeh Kemmerich, Thomas Ahmed, Tarem Baykal, Nazife Middle East Tech Univ METU Grad Sch Informat Ankara Turkey Norwegian Univ Sci & Technol NTNU Dept Informat Secur & Commun Technol Gjovik Norway Independent Univ Bangladesh IUB Dept Comp Sci & Engn Dhaka Bangladesh Univ Bremen Dept Comp Sci Grp Comp Architecture Bremen Germany

Distributed denial-of-service (DDoS) attacks are constantly evolving as the computer and networking technologies and attackers' motivations are changing. In recent years, several supervised DDoS detection algorithms have been proposed. However, these algorithms require a priori knowledge of the classes and cannot automatically adapt to frequently changing network traffic trends. This emphasizes the need for the development of new DDoS detection mechanisms that target zero-day and sophisticated DDoS attacks. In this paper, we propose an online, sequential, DDoS detection scheme that is suitable for use with multivariate data. The proposed algorithm utilizes a kernel-based learning algorithm, the Mahalanobis distance, and a chi-square test. Initially, we extract four entropy-based and four statistical features from network flows per minute as detection metrics. Then, we employ the kernel-based learning algorithm using the entropy features to detect input vectors that were suspected to be DDoS. This algorithm assumes no model for network traffic or DDoS. It constructs and adapts a dictionary of features that approximately span the subspace of normal behavior. Every T minutes, the Mahalanobis distance between suspicious vectors and the distribution of dictionary members is measured. Subsequently, the chi-square test is used to evaluate the Mahalanobis distance. The proposed DDoS detection scheme was applied to the CICIDS2017 dataset, and we compared the results with those given by existing algorithms. It was demonstrated that the proposed online detection scheme outperforms almost all available DDoS classification algorithms with an offline learning process.

关键词： online learning algorithm DDoS KOAD E-KOAD Mahalanobis distance Chi-square test

来源：评论

学校读者我要写书评

暂无评论

Intelligent Positioning Approach for High Speed Trains Based on Ant Colony Optimization and Machine learning algorithms

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第10期20卷 3737-3746页

作者： Cheng, Ruijun Song, Yongduan Chen, Dewang Ma, Xiaoping Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China Fuzhou Univ Coll Math & Comp Sci Key Lab Spatial Data Min & Informat Sharing MOE Fuzhou 350116 Fujian Peoples R China

For high-speed train (HST), high-precision of train positioning is important to guarantee train safety and operational efficiency. For improving train positioning accuracy, we develop a mathematical positioning model by analyzing the wireless position report created by HST. To begin with, k-means algorithm is integrated with the least square support vector machine (LSSVM) to differentiate the position data and establish the corresponding prediction model for each position data class. Then, the ant colony optimization (ACO) algorithm is introduced to adaptively optimize the clustering number of position data and solve the over-fitting problem of the single k-means algorithm. So, a better classification of position data can be obtained by ACO-k-means than the single k-means algorithm. Furthermore, the online learning algorithms are designed for improving the adaptability and real-time performance of established positioning model. Finally, the field data of Beijing-Shanghai high-speed railway (BS_HSR) is used to test the performance of the established positioning models. Experiments on real-world positioning data sets from BS_HSR illustrate that the proposed methods can enhance the real-time performance in online updating process on the premise of reducing the positioning error.

关键词： High-speed train train positioning error LSSVM ant colony optimization (ACO) K-means algorithm online learning algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：