On the neural network approach for forecasting of nonstationary time series on the basis of the Hilbert-Huang transform

2011 ◽  
Vol 72 (7) ◽  
pp. 1405-1414 ◽  
Author(s):  
V. G. Kurbatskii ◽  
D. N. Sidorov ◽  
V. A. Spiryaev ◽  
N. V. Tomin
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Nonstationary Time Series ◽  
Network Approach ◽  
Neural Network Approach ◽  
Hilbert Huang Transform ◽  
The Neural Network

scholarly journals ESTIMATION OF THE LYAPUNOV SPECTRUM FROM ONE-DIMENSIONAL OBSERVATIONS USING NEURAL NETWORKS

2014 ◽  
pp. 30-34
Author(s):  
Vladimir Golovko
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Neural Networks ◽  
Time Series ◽  
Lyapunov Spectrum ◽  
Network Approach ◽  
Neural Network Approach ◽  
One Dimensional ◽  
The Neural Network ◽  
Attractor Dynamics

This paper discusses the neural network approach for computing of Lyapunov spectrum using one dimensional time series from unknown dynamical system. Such an approach is based on the reconstruction of attractor dynamics and applying of multilayer perceptron (MLP) for forecasting the next state of dynamical system from the previous one. It allows for evaluating the Lyapunov spectrum of unknown dynamical system accurately and efficiently only by using one observation. The results of experiments are discussed.

A Combined ARIMA and Neural Network Approach for Time Series Forecasting

2011 ◽  
pp. 213-225 ◽  
Author(s):  
G. Peter Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Neural Network Model ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Network Approach ◽  
Neural Network Approach ◽  
Individual Model ◽  
The Neural Network ◽  
Forecasting Performance

This chapter presents a combined ARIMA and neural network approach for time series forecasting. The model contains three steps: (1) fitting a linear ARIMA model to the time series under study, (2) building a neural network model based on the residuals from the ARIMA model, and (3) combine the ARIMA prediction and the neural network result to form the final forecast. By combining different models, we aim to take advantage of the unique modeling capability of each individual model and improve forecasting performance dramatically. The effectiveness of the combining approach is demonstrated and discussed with three applications.

Investigation of the possibility of reconstructing strain diagrams using the neural network approach

2011 ◽  
Vol 47 (15) ◽  
pp. 1689-1695
Author(s):  
M. B. Bakirov ◽  
O. A. Mishulina ◽  
I. A. Kiselev ◽  
I. A. Kruglov
Keyword(s):  
Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network

An artificial neural network approach to discrete-event simulation

1995 ◽  
Vol 9 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Ian Flood ◽  
Kenneth Worley
Keyword(s):  
Neural Network ◽  
Discrete Event Simulation ◽  
Network Architecture ◽  
Discrete Event ◽  
Network Approach ◽  
Neural Network Approach ◽  
Event Simulation ◽  
The Neural Network ◽  
Component Processes ◽  
And Performance

AbstractThis paper proposes and evaluates a neural network-based method for simulating manufacturing processes that exhibit both noncontinuous and stochastic behavior processes more conventionally modeled, using discrete-event simulation algorithms. The incentive for developing the technique is its potential for rapid execution of a simulation through parallel processing, and facilitation of the development and improvement of models particularly where there is limited theory describing the dependence between component processes. A brief introduction is provided to a radial-Gaussian neural network architecture and training process, the system adopted for the work presented in this paper. A description of the basic approach proposed for applying this technology to simulation is then described. This involves the use of a modularized neural network approach to model construction and the prediction of the occurrence of events using information retained from several previous states of the simulation. A class of earth-moving systems, comprising a push-dozer and a fleet of scrapers, is used as the basis for assessing the viability and performance of the proposed approach. A series of experiments show the neural network to be capable of both capturing the characteristic behavior and making an accurate prediction of production rates of scraper-based earth-moving systems. The paper concludes with an indication of some areas for further development and evaluation of the technique.

Estimation of Annual Average Daily Traffic on Low-Volume Roads: Factor Approach Versus Neural Networks

2000 ◽  
Vol 1719 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Satish C. Sharma ◽  
Pawan Lingras ◽  
Guo X. Liu ◽  
Fei Xu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Traffic Monitoring ◽  
Network Approach ◽  
Annual Average ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Low Volume Roads ◽  
Annual Average Daily Traffic ◽  
Low Volume

Estimation of the annual average daily traffic (AADT) for low-volume roads is investigated. Artificial neural networks are compared with the traditional factor approach for estimating AADT from short-period traffic counts. Fifty-five automatic traffic recorder (ATR) sites located on low-volume rural roads in Alberta, Canada, are used as study samples. The results of this study indicate that, when a single 48-h count is used for AADT estimation, the factor approach can yield better results than the neural networks if the ATR sites are grouped appropriately and the sample sites are correctly assigned to various ATR groups. Unfortunately, the current recommended practice offers little guidance on how to achieve the assignment accuracy that may be necessary to obtain reliable AADT estimates from a single 48-h count. The neural network approach can be particularly suitable for estimating AADT from two 48-h counts taken at different times during the counting season. In fact, the 95th percentile error values of about 25 percent as obtained in this study for the neural network models compare favorably with the values reported in the literature for low-volume roads using the traditional factor approach. The advantage of the neural network approach is that classification of ATR sites and sample site assignments to ATR groups are not required. The analysis of various groups of low-volume roads presented also leads to a conclusion that, when defining low-volume roads from a traffic monitoring point of view, it is not likely to matter much whether the AADT on the facility is less than 500 vehicles, less than 750 vehicles, or less than 1,000 vehicles.

International Real Estate Review

2004 ◽  
Vol 7 (1) ◽  
pp. 121-138
Author(s):  
Xin J. Ge ◽  
◽  
G. Runeson ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hong Kong ◽  
Time Series Data ◽  
Series Data ◽  
Residential Property ◽  
Neural Network Approach ◽  
Price Performance ◽  
The Neural Network ◽  
Property Price

This paper develops a forecasting model of residential property prices for Hong Kong using an artificial neural network approach. Quarterly time-series data are applied for testing and the empirical results suggest that property price index, lagged one period, rental index, and the number of agreements for sales and purchases of units are the major determinants of the residential property price performance in Hong Kong. The results also suggest that the neural network methodology has the ability to learn, generalize, and converge time series.

Modeling of Uncertain Nonlinear System With Z-Numbers

2021 ◽  
pp. 290-314
Author(s):  
Raheleh Jafari ◽  
Sina Razvarz ◽  
Alexander Gegov ◽  
Satyam Paul
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Network Approach ◽  
Uncertain Nonlinear System ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Simulation Results ◽  
Fuzzy Equations

In order to model the fuzzy nonlinear systems, fuzzy equations with Z-number coefficients are used in this chapter. The modeling of fuzzy nonlinear systems is to obtain the Z-number coefficients of fuzzy equations. In this work, the neural network approach is used for finding the coefficients of fuzzy equations. Some examples with applications in mechanics are given. The simulation results demonstrate that the proposed neural network is effective for obtaining the Z-number coefficients of fuzzy equations.

Individualizing Displays through the Use of Neural Networks

1997 ◽  
Vol 41 (1) ◽  
pp. 269-273
Author(s):  
Kai-Chun Cheng ◽  
Ray E. Eberts
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Intelligent Vehicle ◽  
Network Approach ◽  
Neural Network Approach ◽  
Traveler Information ◽  
Gas Stations ◽  
The Neural Network ◽  
Advanced Traveler Information System ◽  
Near Future

An Advanced Traveler Information System (ATIS), a key component of Intelligent Vehicle highway Systems (IVHS) in the near future, will help travelers find locations of restaurants, lodging, gas stations, and rest stops. On typical ATIS displays, which are now being incorporated in some advanced vehicles, the choices for these traveler services are presented to the vehicle occupants alphabetically. An experiment was conducted to determine whether individualizing the display through the use of neural networks enhanced performance when choosing restaurants. The neural network ATIS was compared to an ATIS that displayed the most frequently chosen restaurants at the top, one that alphabetized the list of restaurants, and one that randomly displayed the restaurant choices. The time to choose a restaurant was significantly faster for the individualized displays (neural network and frequency) when compared to the nonindividualized displays (alphabetical and random). When the two individualized displays were compared, choice time was significantly faster for the neural network approach.

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