Prediction of multivariate chaotic time series via radial basis function neural network

Chen, Diyi; Han, Wenting

doi:10.1002/cplx.21441

Cited by 42 publications

(21 citation statements)

References 43 publications

(39 reference statements)

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“…It is worth noting that the soft measurement method is a multivariate modeling process. Moreover, multivariate chaotic time series modeling method contains more information associated with the original dynamic system which can improve the accuracy of prediction to some extent compared with the univariate chaotic time series modeling method [27][28][29].…”

Section: Phase Space Reconstruction (Psr)mentioning

confidence: 99%

“…Many methods, such as false nearest neighbor method [20], G-P algorithm [32,33] and Cao's method [34], are proposed to estimate the value of embedding dimension m. In this paper, we only need to obtain the minimum embedding dimension m min based on the Takens embedding theorem [24][25][26][27][28][29]. The G-P algorithm proposed by Grassberger and Procaccia is adopted to determine it due to its easy calculation and good performance.…”

Section: Phase Space Reconstruction (Psr)mentioning

confidence: 99%

“…In fact, PSR increases the dimensions of the input assistant variables to obtain more information hidden in the chaotic dynamic system [24][25][26][27][28][29]. Using PCA, we can reduce the dimensions of the input assistant variables without information loss to enhance the ability of generalization performance and to avoid over-fitting on account of overmuch input dimensions on the basis of PSR [27].…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

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Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD)

Qiao

2016

Water

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Abstract:The precision of soft measurement for biochemical oxygen demand (BOD) is always restricted due to various factors in the wastewater treatment plant (WWTP). To solve this problem, a new soft measurement modeling method based on chaos theory is proposed and is applied to BOD measurement in this paper. Phase space reconstruction (PSR) based on Takens embedding theorem is used to extract more information from the limited datasets of the chaotic system. The WWTP is first testified as a chaotic system by the correlation dimension (D), the largest Lyapunov exponents (λ 1 ), the Kolmogorov entropy (K) of the BOD and other water quality parameters time series. Multivariate chaotic time series modeling method with principal component analysis (PCA) and artificial neural network (ANN) is then adopted to estimate the value of the effluent BOD. Simulation results show that the proposed approach has higher accuracy and better prediction ability than the corresponding modeling approaches not based on chaos theory.

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Section: Phase Space Reconstruction (Psr)mentioning

confidence: 99%

Section: Phase Space Reconstruction (Psr)mentioning

confidence: 99%

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

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Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD)

Qiao

2016

Water

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“…Baishuihe landslide [53,54], which lies on the southern Yangtze River, is located in Shazhenxi in Zigui county. The bedrock ridges located to the east and west are considered landslide boundaries.…”

Section: Baishuihe Landslide 311 Geological Conditionsmentioning

confidence: 99%

Influencing factor analysis and displacement prediction in reservoir landslides − a case study of Three Gorges Reservoir (China)

Huang

Yin

et al. 2016

Teh. vjesn.

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Subject reviewThe developmental tendencies of cumulative displacement time series associated with reservoir landslides influenced by large water reservoirs must be effectively predicted. However, traditional methods do not encompass the dynamic response relationships between landslide deformation and its influencing factors. Therefore, a new approach based on the exponential smoothing (ES) and multivariate extreme learning machine methods was introduced to reveal the influencing factors of landslide deformation and to forecast landslide displacement values. First, the influencing factors of reservoir landslide deformation were analysed. Second, the ES method was used to predict the trend term displacement and obtain the periodic term displacement by determining the trend term from the cumulative displacement. Next, multivariate influencing factors were analysed to explain the periodic term displacement. Then, an extreme learning machine (ELM) model was established to predict the periodic term displacement based on the multivariable analysis of influencing factors. Finally, cumulative displacement prediction values were obtained by adding the trend and periodic displacement prediction values. The Bazimen and Baishuihe landslides in Three Gorges Reservoir Area (TGRA) were selected as case studies. The proposed ES-multivariate ELM (ES-MELM) model was compared to the ES-univariate ELM (ES-ELM) model. The results show that reservoir landslide deformation is mainly influenced by periodic reservoir water level fluctuations and heavy rainfall. Additionally, the proposed model yields more accurate predictions than the ES-ELM model. Keywords: displacement prediction; exponential smoothing; Extreme Learning Machine; multivariate influencing factors; reservoir landslide; Three Gorges ReservoirAnaliza faktora utjecaja i predviđanje pomaka klizišta akumulacije -studija slučaja Three Gorges Reservoir (Kina)Pregledni članak Potrebno je učinkovito predviđati kako će se vremenski odvijati kumulativni pomaci povezani s klizištima akumulacija. Međutim, tradicionalne metode ne obuhvaćaju dinamički uspostavljene odnose između deformacije klizišta i faktora koji na to utječu. Dakle, uveden je novi pristup na temelju eksponencijalnog izglađivanja (EI) i multivarijatnih metoda ekstremno učećeg stroja kako bi se otkrili čimbenici od utjecaja na deformacije klizišta i predvidjele vrijednosti pomaka klizišta. Prvo su analizirani faktori koji utječu na deformacije klizišta akumulacije. Zatim je EI postupak rabljen za predviđanje trajanja trenda pomaka i dobivanje trajanja periodičnog pomaka određivanjem trajanja trenda iz kumulativnog pomaka. Dalje, analizirani su multivarijatni utjecajni faktori kako bi objasnili trajanje periodičnog pomaka. Nakon toga, postavljen je model ekstremno učećeg stroja kako bi se predvidjelo trajanje periodičnog pomaka na temelju multivarijatne analize utjecajnih čimbenika. Konačno, dobivene su vrijednosti predviđanja kumulativnog pomaka dodavanjem vrijednosti predviđanja trenda i periodičnog pomaka. Bazimen i B...

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“…Briefly, they have predicted time series of Mackey-Glass, gas furnace [16]. On the other hand, Zhao and Yang have performed a study similar to [14], but they have used PSO -SMN to predict time series of Mackey-Glass, gas furnace (Box-Jenkins), and EEG [17].…”

Section: Introductionmentioning

confidence: 99%

Time Series Prediction With a Hybrid System Formed by Artificial Neural Network and Cognitive Development Optimization Algorithm

Köse

Arslan

2018

Scientia Iranica

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Time series prediction is a remarkable research interest, which is widely followed by scientists / researchers. Because many fields include analyzing processes over such time series, different kinds of approaches, methods, and techniques are often employed in order to achieve alternative prediction ways. It seems that Artificial Intelligence oriented solutions have strong potential on providing effective and accurate prediction approaches in even most complicated time series structures. In the sense of the explanations, this study aims to technique introduced by the authors. Here, it has been aimed to predict different kinds of time series, by using the introduced system / approach. In this way it has been possible to discuss about application potential of the hybrid system and report findings related to its success on prediction. The authors believe that the study has been a good chance to support the literature with an alternative solution approach and see potential of a newly developed, Artificial Intelligence oriented optimization algorithm on different applications.

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Prediction of multivariate chaotic time series via radial basis function neural network

Cited by 42 publications

References 43 publications

Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD)

Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD)

Influencing factor analysis and displacement prediction in reservoir landslides − a case study of Three Gorges Reservoir (China)

Time Series Prediction With a Hybrid System Formed by Artificial Neural Network and Cognitive Development Optimization Algorithm

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