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Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination

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International Journal of Minerals,Metallurgy and Materials 2024年第7期31卷 1500-1511页

作者： Dapeng Chen Shenghua Yin Weiguo Long Rongfu Yan Yufei Zhang Zepeng Yan Leiming Wang Wei Chen School of Civil and Resources Engineering University of Science and Technology of BeijingBeijing 100083China Production Technology Department Longshou MineJinchuan Group Co.Ltd.Jinchang 737100China School of Civil Engineering and Architecture East China Jiaotong UniversityNanchang 330013China

Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress *** monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring *** solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock *** three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and *** information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously *** with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then *** this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock *** evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock *** new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou *** moments of mutation in these time series were at least 3 months ahead of the roadway return *** the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0

关键词： deep mining filling body–surrounding rock combination phase space reconstruction multiple time series stability prediction

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Prediction of seawater pH by bidirectional gated recurrent neural network with attention under phase space reconstruction:case study of the coastal waters of Beihai,China

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Acta Oceanologica Sinica 2023年第10期42卷 97-107页

作者： Chongxuan Xu Ying Chen Xueliang Zhao Wenyang Song Xiao Li Hebei Province Key Laboratory of Test/Measurement Technology and Instrument School of Electrical EngineeringYanshan UniversityQinhuangdao 066004China Center for Hydrogeology and Environmental Geology China Geological SurveyGeological Environment Monitoring Engineering Technology Innovation Center of the Ministry of Natural ResourcesBaoding 071051China

Marine life is very sensitive to changes in *** slight changes can cause ecosystems to ***,understanding the future pH of seawater is of great significance for the protection of the marine *** present,the monitoring method of seawater pH has been ***,how to accurately predict future changes has been lacking effective *** on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH *** verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the *** the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation *** R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the *** results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction *** prediction method in this paper can be further expanded and used to predict other marine environmental indicators.

关键词： seawater pH prediction Bi-gated recurrent neural(GRU)model phase space reconstruction attention mechanism improved complete ensemble empirical mode decomposition with adaptive noise

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phase space reconstruction of chaotic dynamical system based on wavelet decomposition

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Chinese Physics B 2011年第2期20卷 114-118页

作者：游荣义黄晓菁 Department of Physics School of ScienceJimei University

In view of the disadvantages of the traditional phase space reconstruction method, this paper presents the method of phase space reconstruction based on the wavelet decomposition and indicates that the wavelet decomposition of chaotic dynamical system is essentially a projection of chaotic attractor on the axes of space opened by the wavelet filter vectors, which corresponds to the time-delayed embedding method of phase space reconstruction proposed by Packard and Takens. The experimental results show that, the structure of dynamical trajectory of chaotic system on the wavelet space is much similar to the original system, and the nonlinear invariants such as correlation dimension, Lyapunov exponent and Kolmogorov entropy are still reserved. It demonstrates that wavelet decomposition is effective for characterizing chaotic dynamical system.

关键词： chaotic dynamical system phase space reconstruction wavelet decomposition

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Prediction of elevator traffic flow based on SVM and phase space reconstruction

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Journal of Harbin Institute of Technology(New Series) 2011年第3期18卷 111-114页

作者：唐海燕齐维贵丁宝 School of Electrical Engineering and Automation Harbin Institute of Technology

To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase space reconstruction has been proposed for ***,the phase space reconstruction for elevator traffic flow time series (ETFTS) is ***,the small data set method is applied to calculate the largest Lyapunov exponent to judge the chaotic property of *** prediction model of ETFTS based on SVM is ***,the method is applied to predict the time series for the incoming and outgoing passenger flow respectively using ETF data collected in some ***,it is compared with RBF neural network *** results show that the trend of factual traffic flow is better followed by predictive traffic *** algorithm has much better prediction *** fitting and prediction of ETF with better effect are realized.

关键词： support vector machine phase space reconstruction prediction of elevator traffic flow RBF neural network

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Selection of Embedding Dimension and Delay Time in phase space reconstruction

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Frontiers of Electrical and Electronic Engineering in China 2006年第1期1卷 111-114页

作者： MA Hong-guang HAN Chong-zhao Research Institute of High Technology Xi’an 710025China

A new algorithm is proposed for computing the embedding dimension and delay time in phase space *** makes use of the zero of the nonbias multiple autocorrelation function of the chaotic time series to determine the time delay,which efficiently depresses the computing error caused by tracing arbitrarily the slop variation of average displacement(AD)in AD ***,by means of the iterative algorithm of multiple autocorrelation andΓtest,the near-optimum parameters of embedding dimension and delay time are *** algorithm is provided with a sound theoretic basis,and its computing complexity is relatively lower and not strongly dependent on the data *** simulated experimental results indicate that the relative error of the correlation dimension of standard chaotic time series is decreased from 4.4%when using conventional algorithm to 1.06%when using this *** accuracy of invariants in phase space reconstruction is greatly improved.

关键词： phase space reconstruction embedding dimension delay time multiple autocorrelation Γtest

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Remaining useful life prediction of lithium-ion batteries based on phase space reconstruction and optimized LSTM network

Remaining useful life prediction of lithium-ion batteries ba...

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第40届中国控制会议

作者： Qizheng Qian Yong Zhang Xiujuan Zheng Jin Xie Weiguang Hao School of Information Science and Engineering Wuhan University of Science and Technology

To solve the problems of insufficient adaptability and low prediction accuracy for lithium-ion battery remaining useful life（RUL） prediction,this paper proposes an optimized neural network prediction method to improve prediction ***,the original signal is used to decompose and reduce noise by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise（CEEMDAN）,and extract the residual that best reflects the degradation trend as a ***,the phase space reconstruction method can determine the most appropriate sliding window size and reconstruct a new sample space to improve prediction ***,the genetic algorithm is applied to perform an adaptive global search for the optimal key parameters of Long Short-Term Memory（LSTM） network,which improves the adaptability of the method on RUL *** order to verify effectiveness,the model was actually applied to NASA batteries,and the experimental results show that the proposed method has higher accuracy and generality than other traditional methods.

关键词： Remaining useful life prediction phase space reconstruction Genetic algorithm Long short-term memory

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Chaotic Time Series Prediction Based on phase space reconstruction and LSSVR Model

Chaotic Time Series Prediction Based on Phase Space Reconstr...

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第三十届中国控制会议

作者： QIAO Meiying~(1,2),MA Xiaoping~2,TAO Hui~1 1.School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo,Henan 454000,P.R.China 2.School of Information and Electrical Engineering,China University Mining and Technology,Xuzhou,Jiangsu 221116,P.R.China

＜正＞At present chaotic time series prediction methods are mainly based on reconstructed phase ***,when the space is reconstructed,two parameters must be determined in advance,they are embedding dimension and delay *** this problem in the paper authors first introduces the minimum differential Entropy ratio principle to determine the embedding dimension and delay time,and advantage of this method is two parameters simultaneously is ***,the phase space can be reconstructed by using the known embedding dimension and delay *** time series can be predicted using well-established LSSVR model in the reconstructed phase ***,in MATLAB2009b environment,the algorithm is verified through the Mackey-Glass time-series data and the actual gas emission time-series *** results show that the geometric meaning is clear and program is simple by minimum differential Entropy ratio principle to determine the embedding dimension and delay *** time-series prediction accuracy is obtained in this reconstructed phase space,and the same high accuracy also can be obtained in short-term prediction the mining face gas emission.

关键词： Chaotic Time Series phase space reconstruction Short-term prediction LSSVR model

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Design of a prediction system based on the dynamical feed-forward neural network

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Science China(Information Sciences) 2023年第1期66卷 43-59页

作者： Xiaoxiang GUO Weimin HAN Jingli REN Henan Academy of Big Data Zhengzhou University Department of Mathematics University of Iowa

Analysis and prediction of time series play a significant role in scientific fields of meteorology,epidemiology, and economy. Efficient and accurate prediction of signals can give an early detection of abnormal variations, provide guidance on preparing a timely response and avoid presumably adverse *** this paper, a prediction system is designed based on the dynamical feed-forward neural network. The trajectory information in the reconstructed phase space, which is topologically equivalent to the dynamical evolution of the system, is applied to establish the prediction model. Moreover, an integer constrained particle swarm optimization algorithm is employed to select the optimal time delay, which is the parameter of our system. Simulation results for applications on the Lorenz system, stock market index, and influenza data indicate that our proposed method can produce efficient and reliable predictions.

关键词： prediction system phase space reconstruction topological equivalence dynamical feed-forward neural network integer constrained particle swarm optimization algorithm

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Nonlinear chaotic characteristic in leaching process and prediction of leaching cycle period

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Journal of Central South University 2016年第11期23卷 2935-2940页

作者：刘超吴爱祥尹升华陈勋 State Key Laboratory of Ministry of Education of China for High-Efficient Mining and Safety of Metal Mines (University of Science and Technology Beijing) Beijing 100083 China

A laboratory leaching experiment with samples of different grades was carried out, and an analytical method of concentration of leaching solution was put forward. For each sample, respectively, by applying phase space reconstruction for time series of monitoring data, the saturated embedding dimension and the correlation dimension were obtained, and the evolution laws between neighboring points in the reconstructed phase space were revealed. With BP neural network, a prediction model of concentration of leaching solution was set up and the maximum error of which was less than 2%. The results show that there exist chaotic characteristics in leaching system, and samples of different grades have different nonlinear dynamic features; the higher the grade of sample, the smaller the correlation dimension; furthermore, the maximum Lyapunov index, energy dissipation and chaotic extent of the leaching system increase with grade of the sample; by phase space reconstruction, the subtle change features of concentration of leaching solution can be magnified and the inherent laws can be fully demonstrated. According to the laws, a prediction model of leaching cycle period has been established to provide a theoretical foundation for solution mining.

关键词： leaching system phase space reconstruction chaotic characteristic leaching cycle period neural network prediction

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Inatorial forecasting method considering macro and micro characteristics of chaotic traffic flow

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Chinese Physics B 2023年第10期32卷 350-362页

作者：侯越张迪李达杨萍 School of Electronics and Information Engineering Lanzhou Jiaotong UniversityLanzhou 730070China

Traffic flow prediction is an effective strategy to assess traffic conditions and alleviate traffic congestion. Influenced by external non-stationary factors and road network structure, traffic flow sequences have macro spatiotemporal characteristics and micro chaotic characteristics. The key to improving the model prediction accuracy is to fully extract the macro and micro characteristics of traffic flow time sequences. However, traditional prediction model by only considers time features of traffic data, ignoring spatial characteristics and nonlinear characteristics of the data itself, resulting in poor model prediction performance. In view of this, this research proposes an intelligent combination prediction model taking into account the macro and micro features of chaotic traffic data. Firstly, to address the problem of time-consuming and inefficient multivariate phase space reconstruction by iterating nodes one by one, an improved multivariate phase space reconstruction method is proposed by filtering global representative nodes to effectively realize the high-dimensional mapping of chaotic traffic flow. Secondly, to address the problem that the traditional combinatorial model is difficult to adequately learn the macro and micro characteristics of chaotic traffic data, a combination of convolutional neural network(CNN) and convolutional long short-term memory(ConvLSTM) is utilized for capturing nonlinear features of traffic flow more comprehensively. Finally,to overcome the challenge that the combined model performance degrades due to subjective empirical determined network parameters, an improved lightweight particle swarm is proposed for improving prediction accuracy by optimizing model hyperparameters. In this paper, two highway datasets collected by the Caltrans Performance Measurement System(PeMS)are taken as the research objects, and the experimental results from multiple perspectives show that the comprehensive performance of the method proposed in this

关键词： traffic flow prediction phase space reconstruction particle swarm optimization algorithm deep learning models

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