Chaotic time series prediction using phase space reconstruction based conceptor network

Zhang, Anguo; Zheng, Xu

doi:10.1007/s11571-020-09612-7

Cited by 20 publications

(7 citation statements)

References 24 publications

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“…In addition, since the construction characteristics of the periodic method are mainly applied in the field of life sciences and the characteristics of various methods to construct complex networks in the time series are considered, this study selects the PSR theory to study the time series of traffic flow. PSR theory recommends how to recover the attractor of dynamic system to determine the time series, which stretches and folds the trajectory in high-dimensional space, and analyzes its dynamic properties [17][18][19]. The most widely used PSR method at this stage is the time delay state space reconstruction method.…”

Section: Time Series Network Methods Based On Psr Theorymentioning

confidence: 99%

Traffic Flow Characteristics mining of Time Series Data Based on Phase Space Reconstruction Using Complex Networks

Xing¹

2021

converter

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Traffic flow time series is vital for mining the traditional statistical characteristics by using the theory of statistics and machine learning when its identity is a special time series. The network analysis of the traffic flow time series, who uses the complex network of time series analysis method, is designed to inquire into the special law of traffic flow time series which uses its visualization characteristics. Through the network analysis of traffic data flow, the connotation of traffic data flow can be revealed, and the relationship between all data and some data can be further studied. Therefore, it is constructed by combination with the phase space reconstruction theory. The phase space trajectory may be squeezed and the structure of attractor may change. We need to use C-C method to estimate the time delay according to the characteristics of integral parameters, and use G-P algorithm to estimate the embedding dimension to avoid it. This study can effectively reveal the motion law of the system. After constructing the complex network of traffic flow time series with various traffic parameters, the degree distribution, clustering coefficient and modularization of the representative critical threshold corresponding network are statistically analysed. The analysis results show that the new networked structure of traffic flow time series proposed in this study has strong advantages, and its core is phase space reconstruction, which can well reflect the information space of traffic dynamic fluctuation. The time series networking method based on phase space reconstruction has become a new approach to inquire into the characteristics of traffic flow time series. The degree distribution of the actual multi-traffic parameter time series construction network satisfies the characteristics of a Gaussian distribution. Their average clustering coefficients have attenuation characteristics, and their modularization degree is obvious.

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Section: Time Series Network Methods Based On Psr Theorymentioning

confidence: 99%

Traffic Flow Characteristics mining of Time Series Data Based on Phase Space Reconstruction Using Complex Networks

Xing¹

2021

converter

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“…Lorentz signal is used to expose nonlinear characteristics of the bolt joints during piezoelectric active sensing. Each dimensional signal component of the Lorentz signal contains all dynamical information of the structure, 54 hence the three-dimensional signal under high-frequency responses is analyzed here. MVMD comprehensively extracts dynamical characteristics of the collected signal, leading to accurate monitoring of bolt joints.…”

Section: The Proposed Looseness Monitoring Approach Of Multiple M1 Bo...mentioning

confidence: 99%

Looseness monitoring of multiple M1 bolt joints using multivariate intrinsic multiscale entropy analysis and Lorentz signal-enhanced piezoelectric active sensing

Yuan

Wang

et al. 2022

Structural Health Monitoring

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Bolts are widely used in the fields of mechanical, civil, and aerospace engineering. The condition of bolt joints has a significant impact on the safe and reliable operation of the whole equipment. The failure of bolt joints monitoring leads to severe accidents or even casualties. This paper proposes a novel bolt joints monitoring method using multivariate intrinsic multiscale entropy (MIME) analysis and Lorentz signal-enhanced piezoelectric active sensing. Lorentz signal is used as excitation signal in piezoelectric active sensing to expose nonlinear dynamical characteristics of the bolt joints. Multivariate variational mode decomposition (MVMD) is employed to decompose multiple components of the collected Lorentz signal into multivariate band-limited intrinsic mode functions (BLIMFs). Afterward, improved multiscale sample entropy (IMSE) values of each channel’s BLIMFs are computed to measure its irregularity and complexity. IMSE values are taken as quantitative features, reflecting dynamical characteristics of bolt joints. Further, the constructed 3-layer feature matrices are adopted as the input of the convolutional neural network (CNN) to achieve accurate bolt joint monitoring. The multiple M1 bolt joints are used during the experiment to verify the effectiveness and superiority of the proposed approach. The results demonstrate the proposed novel approach is promising in bolt joints monitoring.

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“…For example, it has been employed for improving the classification performance of RNNs [21] and CNNs [22,23]. It has also been applied to the time series prediction [24,25], to overcome the catastrophic interference in multi-task learning [26], and to improve the performance of cache-based communications [27].…”

Section: B Conceptor Learningmentioning

confidence: 99%

Conceptor Learning for Class Activation Mapping

Qian¹,

Yang²,

Zhang³

et al. 2022

Preprint

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Class Activation Mapping (CAM) has been widely adopted to generate saliency maps which provides visual explanations for deep neural networks (DNNs). The saliency maps are conventionally generated by fusing the channels of the target feature map using a weighted average scheme. It is a weak model for the inter-channel relation, in the sense that it only models the relation among channels in a contrastive way (i.e., channels that play key roles in the prediction are given higher weights for them to stand out in the fusion). The collaborative relation, which makes the channels work together to provide cross reference, has been ignored. Furthermore, the model has neglected the intra-channel relation thoroughly. In this paper, we address this problem by introducing Conceptor learning into CAM generation. Conceptor leaning has been originally proposed to model the patterns of state changes in recurrent neural networks (RNNs). By relaxing the dependency of Conceptor learning to RNNs, we make Conceptor-CAM not only generalizable to more DNN architectures but also able to learn both the inter-and intra-channel relations for better saliency map generation. Moreover, we have enabled the use of Boolean operations to combine the positive and pseudo-negative evidences, which has made the CAM inference more robust and comprehensive. The effectiveness of Conceptor-CAM has been validated with both formal verifications and experiments on the dataset of the largest scale in literature. The experimental results show that Conceptor-CAM is compatible with and can bring significant improvement to all well recognized CAM-based methods, and has outperformed the state-of-the-art methods by 43.14% ∼ 72.79% (88.39% ∼ 168.15%) on ILSVRC2012 in Average Increase (Drop), 15.42% ∼ 42.55% (47.09% ∼ 372.09%) on VOC, and 17.43% ∼ 31.32% (47.54% ∼ 206.45%) on COCO, respectively.

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Chaotic time series prediction using phase space reconstruction based conceptor network

Cited by 20 publications

References 24 publications

Traffic Flow Characteristics mining of Time Series Data Based on Phase Space Reconstruction Using Complex Networks

Traffic Flow Characteristics mining of Time Series Data Based on Phase Space Reconstruction Using Complex Networks

Looseness monitoring of multiple M1 bolt joints using multivariate intrinsic multiscale entropy analysis and Lorentz signal-enhanced piezoelectric active sensing

Conceptor Learning for Class Activation Mapping

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