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A Prediction Method for Underwater Acoustic Chaotic Signal Based on RBF Neural Network

Guohui Li, Hong Yang
2014 Journal of Software  
A prediction method for underwater acoustic chaotic signal based on RBF neural network is proposed in this paper according to the characteristics of chaotic signal with the short-term prediction.  ...  Index Terms-chaotic signal, phase space reconstruction, RBF neural network, prediction  ...  A PREDICTION METHOD BASED ON RBF NEURAL NETWORK Reconstruction phase space problem is transformed into approximate one step prediction function ψ problem.  ... 
doi:10.4304/jsw.9.6.1581-1586 fatcat:txylhtm4qvdubhqpo3zobyzoye

Forecast of the Employment Situation of College Graduates Based on the LSTM Neural Network

Xing Li, Ting Yang, Syed Hassan Ahmed
2021 Computational Intelligence and Neuroscience  
In this paper, a forecast technique of graduates' employment situation based on the long short-term memory (LSTM) recurrent neural network is conceived, including network structure design, network training  ...  In addition, aiming at minimizing the forecasting error, an LSTM forecasting model parameter optimization algorithm based on multilayer grid search is conceived.  ...  Figures 4-6 show the point-to-point static prediction effect. LSTM neural network based on wavelet reconstruction data can better predict the long-term dynamic trend of financial time series data.  ... 
doi:10.1155/2021/5787355 pmid:34616445 pmcid:PMC8490037 fatcat:2f2szh7ibbge5kkxubgyilh7mi

Short-Term Traffic Prediction Based on Genetic Algorithm Improved Neural Network

2020 Tehnički Vjesnik  
This paper takes the time series of short-term traffic flow as research object.  ...  The delay time and embedding dimension are calculated by C-C algorithm, and the chaotic characteristics of the time series are verified by small data sets method.Then based on the neural network prediction  ...  SHORT-TERM TRAFFIC PREDICTION BASED ON PHASE SPACE RECONSTRUCTION NEURAL NETWORK Neural network is applied to short-term traffic prediction by scholars for its strong self-learning ability.  ... 
doi:10.17559/tv-20180402112949 fatcat:o3sy2db2l5f7llp67kgsmew7t4


2019 Zenodo  
Keywords: Chaos theory, nonlinear dynamics, nonlinear time series analysis, chaos identification, Lyapunov exponent, neural networks prediction of chaotic time series, multilayer, neural networks of support  ...  The chaos theory aims to explain and to predict in a short time the seemingly random and unpredictable behavior of the systems Nonlinear.  ...  A system whose behavior was considered until recently random becomes predictable short term.  ... 
doi:10.5281/zenodo.3592373 fatcat:2kciyib2mjgjhes7zlweqm7uru

Study on Gas Emission Rate Prediction Based on Chaos Analysis

Liu Yang, Shi Qingjun, Li Jing, Ma Huibin, Liu Desheng
2011 Procedia Engineering  
In phase space, the prediction model base on both local region method and global method to realize the short-term prediction on the gas emission rate.  ...  The global method based on the BP neural network shows a good performance. Thus, the application of the chaos theory to the prediction on the gas emission rate is feasible.  ...  steps, indicating the prediction method based on the chaos analysis unable to finish a long-term prediction.  ... 
doi:10.1016/j.proeng.2011.11.2610 fatcat:5isjmjzvo5fanefco6ffxj6f2q

Prediction Model of Weekly Retail Price for Eggs Based on Chaotic Neural Network

Zhe-min LI, Li-guo CUI, Shi-wei XU, Ling-yun WENG, Xiao-xia DONG, Gan-qiong LI, Hai-peng YU
2013 Journal of Integrative Agriculture  
Based on the weekly retail prices of eggs from Jan. 2008 to Dec. 2012, this paper establishes a short-term prediction model of weekly retail prices of eggs based on chaotic neural network.  ...  The empirical result also shows that the chaotic neural network can be widely used in the field of short-term prediction of agricultural prices.  ...  to construct the regression model and forecast short-term egg prices, making a valuable exploration to the short-term price prediction of agricultural product.  ... 
doi:10.1016/s2095-3119(13)60610-3 fatcat:ckivaw2mb5dwdo3maguaq3qkpu

A Hybrid Short-term Traffic Flow Forecasting Method Based on Neural Networks Combined with K-Nearest Neighbor

Zhao Liu, Jianhua Guo, Jinde Cao, Yun Wei, Wei Huang
2018 Promet (Zagreb)  
This paper proposes a hybrid forecasting method based on neural networks combined with the K-nearest neighbor (K-NN) method for short-term traffic flow forecasting.  ...  Based on this point, the K-NN method was employed to reconstruct the training data for neural network models while considering the similarity of traffic flow patterns.  ...  A Bayesian inference-based dynamic linear model was presented to predict online short-term travel time and investigate the uncertainty of travel time prediction [30] .  ... 
doi:10.7307/ptt.v30i4.2651 fatcat:ebm4tof47fhfnhap727tuulfh4

Unsupervised and Generic Short-Term Anticipation of Human Body Motions [article]

Kristina Enes, Hassan Errami, Moritz Wolter, Tim Krake, Bernhard Eberhardt, Andreas Weber, Jörg Zimmermann
2019 arXiv   pre-print
short anticipation times (<0.4 sec) to a recurrent neural network based method.  ...  Various neural network based methods are capable of anticipating human body motions from data for a short period of time.  ...  evaluated the impact on short-term anticipation using a large real world human motion data base and comparing the performance to a state of the art RNN model.  ... 
arXiv:1912.06688v1 fatcat:j3v7zgizdnf45dfwdyi2gfuzoa

Composite Recurrent Neural Networks for Long-Term Prediction of Highly-Dynamic Time Series Supported by Wavelet Decomposition [chapter]

Pilar Gomez-Gil, Angel Garcia-Pedrero, Juan Manuel Ramirez-Cortes
2010 Studies in Computational Intelligence  
In this chapter we present a new neural network architecutre, called (Hybrid and based-on-Wavelet-Reconstructions Network (HWRN) which is able to perform long-term prediction, using recursive prediction  ...  Therefore several non-linear prediction strategies have been developed, many of them based on soft computing.  ...  In this chapter we present a novel neural prediction system called HWRN (Hybrid and based-on-Wavelet-Reconstructions Network).  ... 
doi:10.1007/978-3-642-15534-5_16 fatcat:zk3wfhdhsff2pnbvxoqne5fbku

Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network

Dongxiao Niu, Yan Lu, Xiaomin Xu, Bingjie Li
2015 Mathematical Problems in Engineering  
It predicted the load sharp degree sequence based on the forecasting model to realize the positioning of short-term load inflection point.  ...  Then this paper designed a forecasting model based on the chaos theory and RBF neural network.  ...  But its inherent nonlinear dynamics structure makes it meet the short-term predictability [28] .  ... 
doi:10.1155/2015/231765 fatcat:s2yforyryrgjvfqy5guhwffoua

Data-based prediction and causality inference of nonlinear dynamics [article]

Huanfei Ma, Siyang Leng, Luonan Chen
2017 arXiv   pre-print
but also predict future dynamics.  ...  Particularly, the cutting-edge method to deal with short-term time series data will be focused. Finally, the advantages as well as the remaining problems in this field are discussed.  ...  (b) Sketch of inverse embedding method to predict the dynamics based on short-term high-dimensional time series data.  ... 
arXiv:1710.11318v2 fatcat:4st3pv7wcjdrjbff34bguwjpj4

Local and Global Iterative Algorithms for Real-Time Short-Term Traffic Prediction [chapter]

Eleni Vlahogianni, Matthew Karlaftis
2010 Urban Transport and Hybrid Vehicles  
The bulk of research in short-term traffic flow prediction has concentrated on data-driven time-series models that construct the underlying rules of complex traffic datasets rather than working based on  ...  Introduction When considering short-term prediction systems that operate in real-time and in an "intelligent" technology-based environment, the effectiveness depends, mostly, on predicting traffic information  ...  Local and Global Iterative Algorithms for Real-Time Short- Term Traffic Prediction, Urban Transport and Hybrid Vehicles, Seref Soylu (Ed.), ISBN: 978-953-307-100-8, InTech, Available from:  ... 
doi:10.5772/10178 fatcat:qqbrckkycnh5leqhoupqoxdft4

Combined Prediction of Wind Power with Chaotic Time Series Analysis

Wang Qiang, Yang Yang
2014 Open Automation and Control Systems Journal  
Wind power prediction is one of the most significant technologies to promote the capability of the whole power system that takes in wind electricity.  ...  A combined model for wind power forecasting is presented to decrease the influence of reconstructed parameters by chaotic time series analysis and the neural networks (NNs) in this work.  ...  Section 3 discusses the establishment of prediction models, including linear combination prediction and neural network combination prediction based on phase space reconstruction.  ... 
doi:10.2174/1874444301406010117 fatcat:f47rxsksd5eabgbgucjynb4nfe

Training Algorithm for Neuro-Fuzzy Network Based on Singular Spectrum Analysis [article]

Yulia S. Maslennikova, Vladimir V. Bochkarev
2014 arXiv   pre-print
In this article, we propose a combination of an noise-reduction algorithm based on Singular Spectrum Analysis (SSA) and a standard feedforward neural prediction model.  ...  That increases long-term predictability of the processed dataset comparison with the raw dataset. The method was applied to predict the International sunspot number RZ time series.  ...  ANN long-term prediction model based on SSA was reported by Gholipour et al. [5] .  ... 
arXiv:1410.1151v1 fatcat:e4jzd4dlrbg7nj2pvleynm4irq

Unsupervised and Generic Short-Term Anticipation of Human Body Motions

Kristina Enes, Hassan Errami, Moritz Wolter, Tim Krake, Bernhard Eberhardt, Andreas Weber, Jörg Zimmermann
2020 Sensors  
very short anticipation times ( < 0 . 4 sec) than a recurrent neural network based method.  ...  Various neural network based methods are capable of anticipating human body motions from data for a short period of time.  ...  evaluated the impact on short-term anticipation using a large real world human motion data base and comparing the performance to a state of the art RNN model.  ... 
doi:10.3390/s20040976 pmid:32059396 pmcid:PMC7070907 fatcat:mxwy4mtzm5ae3ahq6vdafxy274
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