Prediction models for network multi-source dissemination of information based on multivariate chaotic time series

Baosong, Mi; Song, Chenguang

doi:10.1109/compcomm.2017.8322647

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…When compared with the back propagation (BP) neural network [23] and double hidden layer neural network, HPABE method is more accurate in estimating chaotic time series, which is at least 10 orders of magnitude higher than the above estimation method. Especially, compared with wavelet neural network (WNN) [24], the estimation accuracy of HPABE method can be 12–13 orders of magnitude higher. When HPABE method is compared with the radial base function (RBF) [25] method, the maximum error result obtained by HPABE method is smaller, and its accuracy can be 7–9 orders of magnitude higher.…”

Section: Simulation Analysismentioning

confidence: 99%

A high‐precision adaptive blind estimation method for chaotic time series

2022

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To improve the estimation accuracy of chaotic time series and reduce the computational complexity, a high-precision adaptive blind estimation method is proposed. By introducing the pre-treatment method, the adaptive identification model is established, and the linear estimation equation is derived. Through the organic combination of the model and the least square mechanism, the complexity of the model is limited. When changing the type and length of the sequence, the parameters can be changed adaptively, so as to construct the corresponding trajectory equation and realize the blind estimation of chaotic time series. The analysis and experimental results show that the estimation accuracy of this method can reach 10 -16 order of magnitude.

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Section: Simulation Analysismentioning

confidence: 99%

A high‐precision adaptive blind estimation method for chaotic time series

2022

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“…Over the last decade, CTSP has been applied to the study of blood glucose, disease, and gait in humans [5]- [7]. Besides, CTSP also has been applied to cyber-information tasks such as retweeting [8], information diffusion [9], and DoS and DDoS attack detection [10]. The application of CTSP in the real world is becoming more significant and more widespread.…”

Section: Introductionmentioning

confidence: 99%

Deep Hybrid Neural Network and Improved Differential Neuroevolution for Chaotic Time Series Prediction

Huang

Yuan

2020

IEEE Access

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Chaos is widespread in non-linear systems such as finance, energy, and weather. In the chaos system, a variable changing with time generates a chaotic time series, which contains a wealth of information about the non-linear system, and it is helpful for us to analyze and understand chaos systems. Traditional hybrid models for chaotic time series prediction based on neural networks have problems such as low prediction accuracy and difficulty in determining the network topologies. In recent years, the chaotic time series prediction has attached the attention of researchers in the area of deep learning. In this paper, we use a deep hybrid neural network (DHNN) based on convolutional neural network (CNN), gated recurrent unit (GRU) network, and attention mechanism to predict chaotic time series. Besides, we use the idea of neuroevolution to optimize the topologies of the DHNN. In the DHNN, we use CNN to capture spatial features from phase space reconstruction of chaotic time series. Then, we combine spatial features with the original chaotic time series. GRU extracts the spatio-temporal features from the combined sequence, and an attention mechanism with a non-linear activation function is designed to capture critical spatio-temporal features. Besides, we propose an improved differential evolution (IDE) algorithm to optimize the topologies of the DHNN, including the filter sizes of CNN and the number of hidden neurons of GRU. We develop the IDE with an adaptive mutation operator and dynamic chaos crossover operator, which can improve convergence speed and reduce optimization time. In this paper, we use the theoretical Lorenz datasets, monthly mean total sunspot datasets, and the actual coalmine gas concentration datasets to verify the prediction accuracy of the proposed prediction model. Experimental results have shown that the proposed prediction model performs well in chaotic time series forecasting. INDEX TERMS Chaotic time series prediction, convolutional neural network, gated recurrent unit, attention mechanism, improved differential evolution, neuroevolution

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Prediction models for network multi-source dissemination of information based on multivariate chaotic time series

Cited by 2 publications

References 6 publications

A high‐precision adaptive blind estimation method for chaotic time series

A high‐precision adaptive blind estimation method for chaotic time series

Deep Hybrid Neural Network and Improved Differential Neuroevolution for Chaotic Time Series Prediction

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