检索结果-内蒙古大学图书馆

dynamical pattern recognition for sampling sequences based on deterministic learning and structural stability

NEUROCOMPUTING 2021年 458卷 376-389页

作者： Wu, Weiming Zhang, Fukai Wang, Cong Yuan, Chengzhi Shandong Univ Sch Control Sci & Engn Ctr Intelligent Med Engn Jinan 250061 Peoples R China Univ Rhode Isl Dept Mech Ind & Syst Engn Kingston RI 02881 USA

This paper focuses on the recognition problem of dynamical patterns consisting of sampling sequences. Specifically, based on the concept of structural stability, a novel similarity measure for dynamical patterns is first given. Then, a specific realization is provided, which consists of: (1) an approximation scheme for computation of partial derivative information by utilizing the knowledge learned through deterministic learning;(2) a similarity comparison scheme using the recognition errors generated from the discrete-time dynamical estimators;and (3) performance analysis of the recognition scheme with general recognition conditions. Compared with the existing methods, in which misrecognition may occur when the differences of dynamics between adjacent training patterns are very small, the proposed method is more appealing in the sense that, the partial derivatives of dynamics are introduced to complement the similarity measures, such that the recognition performance is much improved. Simulation studies are conducted to verify the proposed method in a relatively large data set. (c) 2021 Published by Elsevier B.V.

关键词： Adaptive dynamics learning Deterministic learning dynamical pattern recognition Structure stability Sampling sequences

来源：评论

学校读者我要写书评

暂无评论

dynamical pattern recognition for univariate time series and its application to an axial compressor

引用

Control Theory and Technology 2024年第1期22卷 39-55页

作者： Jingtao Hu Weiming Wu Zejian Zhu Cong Wang Center for Intelligent Medical Engineering School of Control Science and TechnologyShandong UniversityJinan250061ShandongChina School of Automation Science and Engineering South China University of TechnologyGuangzhou510641GuangdongChina

In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurements of general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe the univariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is then adopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and the modeling results in the form of radial basis function network (RBFN) models are stored in a pattern library. Subsequently, multiple observer-based dynamical estimators containing the RBFN models in the pattern library are constructed for a test univariate time series, and a recognition decision scheme is proposed by the derived recognition indicator. On this basis, more concise recognition conditions are provided, which is beneficial for verifying the recognition results. Finally, simulation studies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach.

关键词： dynamical pattern recognition Deterministic learning Stall warning Radial basis function network Sampled-data observer

来源：评论

学校读者我要写书评

暂无评论

Deterministic learning and rapid dynamical pattern recognition

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS 2007年第3期18卷 617-630页

作者： Wang, Cong Hill, David J. S China Univ Technol Coll Automat Guangzhou 510641 Peoples R China S China Univ Technol Ctr Control & Optimizat Guangzhou 510641 Peoples R China Australian Natl Univ Res Sch Informat Sci & Engn Canberra ACT 0200 Australia

recognition of temporal/dynamical patterns is among the most difficult pattern recognition tasks. In this paper, based on a recent result on deterministic learning theory, a deterministic framework is proposed for rapid recognition of dynamical patterns. First, it is shown that a time-varying dynamical pattern can be effectively represented in a time-invariant and spatially distributed manner through deterministic learning. Second, a definition for characterizing similarity of dynamical patterns is given based on system dynamics inherently within dynamical patterns. Third, a mechanism for rapid recognition of dynamical patterns is presented, by which a test dynamical pattern is recognized as similar to a training dynamical pattern if state synchronization is achieved according to a kind of internal and dynamical matching on system dynamics. The synchronization errors can be taken as the measure of similarity between the test and training patterns. The significance of the paper is that a completely dynamical approach is proposed, in which the problem of dynamical pattern recognition is turned into the stability and convergence of a recognition error system. Simulation studies are included to demonstrate the effectiveness of the proposed approach.

关键词： deterministic learning dynamical pattern recognition representation similarity synchronization

来源：评论

学校读者我要写书评

暂无评论

Observer-based dynamical pattern recognition via deterministic learning

引用

NEURAL NETWORKS 2023年第1期159卷 161-174页

作者： Hu, Jingtao Wu, Weiming Zhang, Fukai Chen, Tianrui Wang, Cong South China Univ Technol Sch Automat Sci & Engn Guangzhou Peoples R China Shandong Univ Ctr Intelligent Med Engn Sch Control Sci & Engn Jinan Peoples R China

In this paper, based on the sampled-data observer and the deterministic learning theory, a rapid dynamical pattern recognition approach is proposed for univariate time series composed of the output signals of the dynamical systems. Specifically, locally-accurate identification of inherent dynamics of univariate time series is first achieved by using the sampled-data observer and the radial basis function (RBF) networks. The dynamical estimators embedded with the learned knowledge are then designed by resorting to the sampled-data observer. It is proved that generated estimator residuals can reflect the difference between the system dynamics of the training and test univariate time series. Finally, a recognition decision-making scheme is proposed based on the residual norms of the dynamical estimators. Through rigorous analysis, recognition conditions are given to guarantee the accurate recognition of the dynamical pattern of the test univariate time series. The significance of this paper lies in that the difficult problems of dynamical modeling and rapid recognition for univariate time series are solved by incorporating the sampled-data observer design and the deterministic learning theory. The effectiveness of the proposed approach is confirmed by a numerical example and compressor stall warning experiments.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： dynamical pattern recognition Deterministic learning Observer Radial basis function network Univariate time series

来源：评论

学校读者我要写书评

暂无评论

New Results on Rapid dynamical pattern recognition via Deterministic Learning From Sampling Sequences

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第9期35卷 12330-12343页

作者： Wu, Weiming Hu, Jingtao Zhang, Fukai Wang, Cong Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China

Rapid dynamical pattern recognition based on the deterministic learning method (DLM-based RDPR) aims to rapidly recognize the most similar dynamical pattern pair from perspectives of differences in inherent system dynamics. The basic mechanism is to use available recognition errors to reflect the differences in the dynamics of dynamical pattern pairs and then to make a decision based on a minimal recognition error (MRE) principle. This article focuses on providing a rigorous theoretical analysis of the MRE principle in DLM-based RDPR under the sampled-data framework. Specifically, we seek a unified methodology from the similarity definition to the measure implementation and then to derive general sufficient conditions and necessary conditions for the MRE principle. The main idea is to: 1) from the average signal energy aspect, define a time-dependent dynamics-based similarity in dynamical pattern pairs and reestablish the measure of recognition errors generated from the DLM-based RDPR;2) introduce the energy-based Lyapunov method to establish the interrelation between the dynamical distance and the recognition error;and 3) derive sufficient conditions and necessary conditions from two directions of the interrelation. The proposed conditions distinguish themselves from virtually all of the existing DLM-based RDPR works with only sufficient conditions in the sense that it is shown in a rigorous analysis that under what conditions, the pattern pair recognized based on the MRE principle is indeed the most similar one. Therefore, the proposed work makes the DLM-based RDPR possess good interpretability and provides strong theoretical guidance in engineering applications.

关键词： pattern recognition System dynamics Learning systems Sufficient conditions Character recognition Trajectory Task analysis Adaptive dynamics learning deterministic learning dynamical pattern recognition sampling sequences

来源：评论

学校读者我要写书评

暂无评论

Implementing dynamical pattern recognition Algorithm on Computer Cluster 35

Implementing Dynamical Pattern Recognition Algorithm on Comp...

引用

第35届中国控制会议

作者： SUN Qinghua LIN Peng WANG Cong College of Automation Science& Engineering South China University of Technology Guangdong Key Laboratory of Biomedical Engineering South China University of Technology

ISBN: (纸本)9781509009107

dynamical pattern recognition based on deterministic learning is the latest pattern recognition *** accomplish large-scale calculation in a short time under this algorithm,high-efficient computing platform is *** computing and computer cluster provide an efficient method of solving this *** this paper,the parallel implementation of dynamical pattern recognition algorithm is accomplished firstly,and then a computer cluster based on MATLAB for this recognition algorithm is *** the end,an experiment about the detection of rotating stall in an axial compressor through dynamic recognition on the cluster is included to demonstrate the effectiveness of the computer *** shows that based on the computer cluster,the parallelization technique can dramatically improve the efficient of the recognition algorithm.

关键词： Parallel algorithm computer cluster deterministic learning dynamical pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Rapid dynamical pattern recognition for sampling sequences

引用

Science China(Information Sciences) 2021年第3期64卷 118-135页

作者： Weiming WU Qian WANG Chengzhi YUAN Cong WANG School of Automation Science and Engineering South China University of Technology School of Control Science and Engineering Shandong University Department of Mechanical Industrial and Systems Engineering University of Rhode Island

In this paper, based on deterministic learning, we propose a method for rapid recognition of dynamical patterns consisting of sampling sequences. First, for the sequences yielded by sampling a periodic or recurrent trajectory(a dynamical pattern) generated from a nonlinear dynamical system, a sampled-data deterministic learning algorithm is employed for modeling/identification of inherent system dynamics. Second,a definition is formulated to characterize similarities between sampling sequences(dynamical patterns) based on differences in the system dynamics. Third, by constructing a set of discrete-time dynamical estimators based on the learned knowledge, similarities between the test and training patterns are measured by using the average L1 norms of synchronization errors, and general conditions for accurate and rapid recognition of dynamical patterns are given in a sampled-data framework. Finally, numerical examples are discussed to illustrate the effectiveness of the proposed method. We demonstrate that not only a test pattern can be rapidly recognized corresponding to a similar training pattern, but also the proposed recognition conditions can be verified step by step based on historical sampling data. This makes a distinction compared with the previous work on rapid dynamical pattern recognition for continuous-time nonlinear systems, in which the recognition conditions are difficult to be verified by using continuous-time signals.

关键词： deterministic learning dynamical pattern recognition sampling sequence synchronization

来源：评论

学校读者我要写书评

暂无评论

DETERMINISTIC LEARNING AND RAPID dynamical pattern recognition

引用

IFAC Proceedings Volumes 2005年第1期38卷 180-185页

作者： Cong Wang David J. Hill College of Automation South China University of Technology Guangzhou 510641 China Research School of Information Sciences and Engineering The Australian National University Canberra ACT 0200 Australia

recognition of temporal/dynamical patterns is among the most difficult pattern recognition tasks. In this paper, based on a recent result on deterministic learning theory, a unified, deterministic approach is proposed for effective representation and rapid recognition of dynamical patterns. Firstly, it is shown that time-varying dynamical patterns can be effectively represented in a time-invariant and spatially-distributed manner through deterministic learning. Then, by characterizing the similarity of dynamical patterns based on the system dynamics inherently within them, a dynamical recognition mechanism is proposed. Rapid recognition of dynamical patterns can be implemented when state synchronization is achieved according to a kind of indirect and dynamical matching on system dynamics. The synchronization errors can be taken as the measure of similarity between the test and training patterns. The significance of the paper is that the problem of dynamical pattern recognition is turned into a problem of stability and convergence of a closed-loop recognition system, so that a completely dynamical approach is presented for rapid recognition of dynamical patterns.

关键词： dynamical pattern recognition deterministic learning persistence of excitation (PE) condition recognition by synchronization

来源：评论

学校读者我要写书评

暂无评论

dynamical Feature Extraction and pattern recognition for Mental Workload Level with FNIRS 17th

Dynamical Feature Extraction and Pattern Recognition for Men...

引用

17th International Conference on Intelligent Robotics and Applications

作者： Zhang, Lianchi Zhang, Jingting Mu, Fengjun Huang, Rui Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9789819607822;9789819607839

Mental Workload Level (MWL) is an important indicator reflecting the human's cognitive state in human-computer interaction, such as the applications of vehicle driving and mental state assessment. A useful method of MWL assessment is by utilizing functional near-infrared spectroscopy (fNIRS) to monitor the brain tissue blood oxygen concentration during human's cognitive process. However, existing fNIRS-based MWL classification methods still have limited performance in terms of classification accuracy and interpretability, since they have not precisely extracted the feature describing human's cognitive behavior, e.g., cerebral hemodynamics. To address this issue, this paper proposes a new dynamical feature extraction and pattern recognition method for MWL classification by using Dynamic System Theory (DST) and Deterministic Learning (DL) technique. A so-called dynamical feature is extracted by modeling the cerebral hemodynamics using fNIRS in human's cognitive process, which is mathematically interpretable and specific to the human'sMWLaccording to DST. dynamical pattern recognition scheme is then proposed by combining DL and Support Vector Machine, aiming to identify high and low levels of MWL with the dynamical feature of fNIRS. Compared to methods such as EEGNet, DeepConvNet, DCNN, SVM, fNIRS-PreT, and Logistic Regression, our method achieved a performance improvement of 2% to 6% for classifying mental workload levels.

关键词： Functional Near-infrared Spectroscopy Mental Workload Level dynamical pattern recognition Deterministic Learning Dynamic System Theory

来源：评论

学校读者我要写书评

暂无评论

Human gait recognition via deterministic learning

引用

NEURAL NETWORKS 2012年 35卷 92-102页

作者： Zeng, Wei Wang, Cong S China Univ Technol Coll Automat Sci & Engn Guangzhou 510641 Guangdong Peoples R China Longyan Univ Sch Phys & Mech & Elect Engn Longyan 364000 Peoples R China

recognition of temporal/dynamical patterns is among the most difficult pattern recognition tasks. Human gait recognition is a typical difficulty in the area of dynamical pattern recognition. It classifies and identifies individuals by their time-varying gait signature data. Recently, a new dynamical pattern recognition method based on deterministic learning theory was presented, in which a time-varying dynamical pattern can be effectively represented in a time-invariant manner and can be rapidly recognized. In this paper, we present a new model-based approach for human gait recognition via the aforementioned method, specifically for recognizing people by gait. The approach consists of two phases: a training (learning) phase and a test (recognition) phase. In the training phase, side silhouette lower limb joint angles and angular velocities are selected as gait features. A five-link biped model for human gait locomotion is employed to demonstrate that functions containing joint angle and angular velocity state vectors characterize the gait system dynamics. Due to the quasi-periodic and symmetrical characteristics of human gait, the gait system dynamics can be simplified to be described by functions of joint angles and angular velocities of one side of the human body, thus the feature dimension is effectively reduced. Locally-accurate identification of the gait system dynamics is achieved by using radial basis function (RBF) neural networks (NNs) through deterministic learning. The obtained knowledge of the approximated gait system dynamics is stored in constant RBF networks. A gait signature is then derived from the extracted gait system dynamics along the phase portrait of joint angles versus angular velocities. A bank of estimators is constructed using constant RBF networks to represent the training gait patterns. In the test phase, by comparing the set of estimators with the test gait pattern, a set of recognition errors are generated, and the average L-1 norms of

关键词： Human gait recognition dynamical pattern recognition Deterministic learning Feature extraction RBF neural network Side silhouette lower limb joint angle and angular velocity

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：