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Model-Based Human Gait recognition Via Deterministic Learning

COGNITIVE COMPUTATION 2014年第2期6卷 218-229页

作者： Zeng, Wei Wang, Cong Li, Yuanqing S China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Peoples R China Longyan Univ Sch Phys & Mech & Elect Engn Longyan 364000 Peoples R China S China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China

In this paper, we present a new model-based approach for human gait recognition in the sagittal plane via deterministic learning (DL) theory. Side silhouette lower limb joint angles characterize the gait system dynamics and are selected as the gait feature. Locally accurate identification of the gait system dynamics is achieved by using radial basis function (RBF) neural networks through DL. 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. A bank of estimators is constructed using constant RBF networks to represent the training gait patterns. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated, and the average L (1) norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. Therefore, the test gait pattern can be rapidly recognized according to the smallest error principle. In contrast to other existing approaches, the main focus of this paper is on obtaining and reusing the knowledge of the gait system dynamics. Finally, experiments are carried out on the CASIA-A and CASIA-B gait databases to benchmark the effectiveness of the proposed approach.

关键词： Human gait recognition dynamical pattern recognition Deterministic learning Feature extraction Side silhouette lower limb joint angles

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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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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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pattern-Based NN Control of a Class of Uncertain Nonlinear Systems

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018年第4期29卷 1108-1119页

作者： Yang, Feifei Wang, Cong South China Univ Technol Sch Mat Sci & Engn Dept Biomed Engn Guangzhou 510641 Guangdong Peoples R China South China Univ Technol Sch Automat Guangzhou 510641 Guangdong Peoples R China South China Univ Technol Key Lab Biomed Engn Guangdong Guangzhou 510641 Guangdong Peoples R China

This paper presents a pattern-based neural network (NN) control approach for a class of uncertain nonlinear systems. The approach consists of two phases of identification and another two phases of recognition and control. First, in the phase (i) of identification, adaptive NN controllers are designed to achieve closed-loop stability and tracking performance of nonlinear systems for different control situations, and the corresponding closed-loop control system dynamics are identified via deterministic learning. The identified control system dynamics are stored in constant radial basis function (RBF) NNs, and a set of constant NN controllers are constructed by using the obtained constant RBF networks. Second, in the phase (ii) of identification, when the plant is operated under different or abnormal conditions, the system dynamics under normal control are identified via deterministic learning. A bank of dynamical estimators is constructed for all the abnormal conditions and the learned knowledge is embedded in the estimators. Third, in the phase of recognition, when one identified control situation recurs, by using the constructed estimators, the recurred control situation will be rapidly recognized. Finally, in the phase of pattern-based control, based on the rapid recognition, the constant NN controller corresponding to the current control situation is selected, and both closed-loop stability and improved control performance can be achieved. The results presented show that the pattern-based control realizes a humanlike control process, and will provide a new framework for fast decision and control in dynamic environments. A simulation example is included to demonstrate the effectiveness of the approach.

关键词： Control situations deterministic learning dynamical pattern recognition pattern-based control radial basis function (RBF) neural network (NN) uncertain nonlinear systems

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Incipient fault detection of nonlinear dynamical systems via deterministic learning

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NEUROCOMPUTING 2018年 313卷 125-134页

作者： Wang, Qian Wang, Cong South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Guangdong Peoples R China

Detection of incipient faults is an important and challenging issue in the area of fault diagnosis and prognosis. This paper presents a new incipient fault detection approach for nonlinear dynamical systems via deterministic learning. Through defining and establishing the banks of health, sub-health and incipient fault modes, the incipient fault detectability condition is derived with the fault mismatch function. The system dynamics underlying three kinds of system modes are accurately approximated via deterministic learning firstly. Secondly, a bank of estimators is constructed using the learned modes. A set of residuals is achieved by comparing the bank of estimators with the monitored system. According to the smallest residual principle and the fault mismatch function, if the average L-1 norm of the residual, which is associated with one of the incipient fault modes, is smaller than the others at a time instant t(d) , the system incipient fault is detected. Finally, the incipient fault detectability is analyzed rigorously. Numerical simulation is investigated to demonstrate the effectiveness of the approach. The significance of the paper lies in that the learned system modes are utilized to achieve rapid detection in the initial stage of incipient fault for nonlinear dynamical systems. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Incipient faults Fault detection dynamical pattern recognition Deterministic learning

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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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Kinect-based Gait recognition System Design Via Deterministic Learning 29

Kinect-based Gait Recognition System Design Via Deterministi...

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第29届中国控制与决策会议

作者： CHENG Fengjiang DENG Muqing WANG Cong College of Automation South China University of Technology Key Laboratory of Biomedical Engineering in Guangdong

ISBN: (纸本)9781509046584

This paper proposes an effective and rapid human gait recognition system based on deterministic learning and Microsoft *** order to deal with the difficulties of feature extraction in the gait recognition system,Kinect sensor is used for realtime skeleton detection and *** deterministic learning algorithm implementation is achieved and a rapid gait recognition scheme is *** graphical programming language of C# and MATLAB GUI controls are combined for building user-friendly and simple interfaces to display the gait training and recognition *** recognition results and detailed parameters can be displayed on the system panels,which is also helpful for further data analysis and algorithm *** effectiveness of the gait recognition system under multi-pattern is verified on the self-constructed gait database.

关键词： Gait recognition dynamical pattern recognition Deterministic Learning Microsoft Kinect2.0

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Rapid Detection of Rotating Stall in Axial Flow Compressors on Computer Cluster 29

Rapid Detection of Rotating Stall in Axial Flow Compressors ...

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第29届中国控制与决策会议

作者： Qinghua Sun Cong Wang Peng Lin College of Automation Science&Engineering South China University of Technology

ISBN: (纸本)9781509046584

Early detection of rotating stall is of great significance to improve the performance of the *** this paper,based on the fault detection decision scheme,early detection of rotating stall is realized via dynamical pattern recognition on computer ***,based on deterministic learning theory,stall detection scheme used for early detection of rotating stall is ***,a PC cluster treated as the computing platform is constructed in ***,an experiment in an axial compressor through dynamic recognition is included to demonstrate the effectiveness of the detection scheme.

关键词： rotating stall deterministic learning dynamical pattern recognition PC cluster

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Rapid Detection of Small Oscillation Faults via Deterministic Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2011年第8期22卷 1284-1296页

作者： Wang, Cong Chen, Tianrui S China Univ Technol Sch Automat Guangzhou 510641 Peoples R China S China Univ Technol Ctr Control & Optimizat Guangzhou 510641 Peoples R China Guangdong Univ Technol Coll Automat Guangzhou 510001 Guangdong Peoples R China

Detection of small faults is one of the most important and challenging tasks in the area of fault diagnosis. In this paper, we present an approach for the rapid detection of small oscillation faults based on a recently proposed deterministic learning (DL) theory. The approach consists of two phases: the training phase and the test phase. In the training phase, the system dynamics underlying normal and fault oscillations are locally accurately approximated through DL. The obtained knowledge of system dynamics is stored in constant radial basis function (RBF) networks. In the diagnosis phase, rapid detection is implemented. Specially, a bank of estimators are constructed using the constant RBF neural networks to represent the training normal and fault modes. By comparing the set of estimators with the test monitored system, a set of residuals are generated, and the average L-1 norms of the residuals are taken as the measure of the differences between the dynamics of the monitored system and the dynamics of the training normal mode and oscillation faults. The occurrence of a test oscillation fault can be rapidly detected according to the smallest residual principle. A rigorous analysis of the performance of the detection scheme is also given. The novelty of the paper lies in that the modeling uncertainty and nonlinear fault functions are accurately approximated and then the knowledge is utilized to achieve rapid detection of small oscillation faults. Simulation studies are included to demonstrate the effectiveness of the approach.

关键词： Deterministic learning dynamical pattern recognition fault detection persistent excitation condition radial basis function neural networks small oscillation faults

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IDENTIFICATION AND recognition OF THE PERSPECTIVE DYNAMIC VISION SYSTEM VIA DETERMINISTIC LEARNING

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2016年第5期12卷 1581-1596页

作者： Wang, Qinghui Zeng, Wei Liu, Fenglin Wang, Ying Longyan Univ Sch Mech & Elect Engn 1 East Xiaobei Rd Longyan 364012 Peoples R China

In this paper, we present a new approach for identification and recognition of the perspective dynamic vision system via deterministic learning theory. The states of the system are periodic or recurrent and only the outputs of the perspective system are measurable. The approach consists of two phases: a training (learning) phase and a test (recognition) phase. In the training phase, a Luenberger-type observer is first used to estimate the unknown system states for the case that the motion parameters of the system are constant, and the estimation error converges exponentially to zero. Then, a radial basis function (RBF) neural network (NN) is used to approximate the unknown dynamics of the vision system when the inputs of the RBF NN are the system states, which makes the RBF network satisfy the localized partial persistence of excitation (PE) condition and the approximation error converges exponentially to a small neighborhood around zero. The obtained knowledge of the approximated vision system dynamics is stored in the constant RBF network. A bank of RBF NN-based Luenberger-type non-linear observers is constructed and served as dynamic representation for the training patterns. In the test phase, when a test dynamical pattern is presented to the RBF NN based observers, a set of recognition errors is generated and the average L-1 norms of the errors will be taken as the similarity measure between the test and training dynamical patterns. Therefore, the test dynamical patterns can be rapidly recognized from a set of training dynamical patterns according to the smallest error principle. Finally, simulation results are included to demonstrate the effectiveness of the proposed scheme.

关键词： Dynamic vision Luenberger-type observer Deterministic learning Similarity definition dynamical pattern recognition

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