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

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

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

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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

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

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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

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Observer-based dynamical pattern recognition via deterministic learning

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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

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Rapid dynamical pattern recognition for sampling sequences

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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

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Implementing dynamical pattern recognition Algorithm on Computer Cluster 35

Implementing Dynamical Pattern Recognition Algorithm on Comp...

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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

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

Dynamical Feature Extraction and Pattern Recognition for Men...

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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

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Time series clustering of dynamical systems via deterministic learning

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第7期15卷 2761-2779页

作者： Sun, Chen Wu, Weiming Zhang, Zirui Li, Zhirui Ji, Bing Wang, Cong Shandong Univ Ctr Intelligent Med Engn Sch Control Sci & Engn Jinan 250061 Peoples R China

A recent deterministic learning theory has achieved locally-accurate identification of unknown system dynamics. This article presents a novel application of deterministic learning theory to unsupervised learning for the first time. Specifically, a new time series clustering strategy with a dynamics-based similarity measure is proposed. Firstly, the dynamics knowledge learned from the time series is represented and stored in the form of constant weights through deterministic learning theory. Secondly, dynamical estimators constructed with the learned dynamics knowledge are used to generate recognition errors, forming a similarity measure matrix to characterize the dynamics-based similarity between time series. Finally, the clustering of time series data with different dynamical behaviors is achieved based on the K-medoids prototype according to the dynamics-based similarity measure matrix. To verify the effectiveness of the proposed method, a dynamical pattern dataset based on benchmark dynamical systems (e.g., Lorenz, Chen, and Lu systems) is also constructed. The experimental results on a synthetic dataset and two real datasets demonstrate that the proposed method is superior to other well-known clustering algorithms in the clustering task for dynamical systems.

关键词： Deterministic learning dynamical pattern recognition Time series clustering dynamical systems Dynamics-based similarity measures

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Direct Representation of the Similarity Measure Based on Deterministic Learning Theory 20th

Direct Representation of the Similarity Measure Based on Det...

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20th Chinese Intelligent Systems Conference

作者： Li, Hao Song, Yunzhong Yang, Feifei Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454003 Henan Peoples R China Henan Int Joint Lab Direct Dr & Gen Intelligent E Zhengzhou Peoples R China Henan Key Lab Intelligent Detect & Control Coal M Jiaozuo Henan Peoples R China Zhengzhou Univ Light Ind Sch Elect Informat Engn Zhengzhou 450002 Peoples R China

ISBN: (纸本)9789819786527;9789819786503;9789819786497

In traditional rapid recognition of dynamical patterns based on the deterministic learning(DL) method, the approximation of the similarity between two dynamical patterns is usually derived by adding the approximation errors and inherent system dynamics of the two systems. In this paper, we attempt to seek the correlation between the system dynamics in the training phase and the recognition errors in the test phase by using inversion methods. A new dynamical pattern recognition method and error recognition system for discrete-time systems are proposed, which can directly represent the similarity between two dynamic patterns.

关键词： dynamical pattern recognition Discrete-time systems Deterministic learning

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Extracting cardiac dynamics within ECG signal for human identification and cardiovascular diseases classification

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NEURAL NETWORKS 2018年 100卷 70-83页

作者： Deng, Muqing Wang, Cong Tang, Min Zheng, Tongjia Hangzhou Dianzi Univ Inst Informat & Control Hangzhou 310018 Zhejiang Peoples R China South China Univ Technol Coll Automat Guangzhou 510640 Guangdong Peoples R China Chinese Acad Med Sci Natl Ctr Cardiovasc Dis Fuwai Hosp State Key Lab Cardiovasc Dis Beijing 100000 Peoples R China Peking Union Med Coll Beijing 100000 Peoples R China Univ Notre Dame Coll Engn Notre Dame IN 46556 USA

Cardiac characteristics underlying the time/frequency domain features are limited and not comprehensive enough to reflect the temporal/dynamical nature of ECG patterns. This paper proposes a dynamical ECG recognition framework for human identification and cardiovascular diseases classification via a dynamical neural learning mechanism. The proposed method consists of two phases: a training phase and a test phase. In the training phase, cardiac dynamics within ECG signals is extracted (approximated) accurately by using radial basis function (RBF) neural networks through deterministic learning mechanism. The obtained cardiac system dynamics is represented and stored in constant RBF networks. An ECG signature is then derived from the extracted cardiac dynamics along the periodic ECG state trajectories. A bank of estimators is constructed using the extracted cardiac dynamics to represent the trained gait patterns. In the test phase, recognition errors are generated and taken as the similarity measure by comparing the cardiac dynamics of the trained ECG patterns and the dynamics of the test ECG pattern. Rapid recognition of a test ECG pattern begins with measuring the state of test pattern, and automatically proceeds with the evolution of the recognition error system. According to the smallest error principle, the test ECG pattern can be rapidly recognized. This kind of cardiac dynamics information represents the beat-to-beat temporal change of ECG modifications and the temporal/dynamical nature of ECG patterns. Therefore, the amount of discriminability provided by the cardiac dynamics is larger than the original signals. This paper further discusses the extension of the proposed method for cardiovascular diseases classification. The constructed recognition system can distinguish and assign dynamical ECG patterns to predefined classes according to the similarity of cardiac dynamics. Experiments are carried out on the FuWai and PTB ECG databases to demonstrate the effecti

关键词： Biometrics ECG pattern recognition Cardiac dynamics dynamical pattern recognition RBF neural network Cardiovascular diseases classification

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