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.

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.

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.

scholarly journals A neural network approach for the simultaneous retrieval of volcanic ash parameters and SO<sub>2</sub> using MODIS data

2014 ◽  
Vol 7 (12) ◽  
pp. 4023-4047 ◽  
Author(s):  
A. Piscini ◽  
M. Picchiani ◽  
M. Chini ◽  
S. Corradini ◽  
L. Merucci ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Volcanic Eruption ◽  
Volcanic Ash ◽  
Thermal Infrared ◽  
Complete Characterization ◽  
Network Approach ◽  
Neural Network Approach ◽  
The Neural Network ◽  
The Neural Networks

Abstract. In this work neural networks (NNs) have been used for the retrieval of volcanic ash and sulfur dioxide (SO2) parameters based on Moderate Resolution Imaging Spectroradiometer (MODIS) multispectral measurements. Different neural networks were built in order for each parameter to be retrieved, for experimenting with different topologies and evaluating their performances. The neural networks' capabilities to process a large amount of new data in a very fast way have been exploited to propose a novel applicative scheme aimed at providing a complete characterization of eruptive products. As a test case, the May 2010 Eyjafjallajókull eruption has been considered. A set of seven MODIS images have been used for the training and validation phases. In order to estimate the parameters associated to the volcanic eruption, such as ash mass, effective radius, aerosol optical depth and SO2 columnar abundance, the neural networks have been trained using the retrievals from well-known algorithms. These are based on simulated radiances at the top of the atmosphere and are estimated by radiative transfer models. Three neural network topologies with a different number of inputs have been compared: (a) three thermal infrared MODIS channels, (b) all multispectral MODIS channels and (c) the channels selected by a pruning procedure applied to all MODIS channels. Results show that the neural network approach is able to estimate the volcanic eruption parameters very well, showing a root mean square error (RMSE) below the target data standard deviation (SD). The network built considering all the MODIS channels gives a better performance in terms of specialization, mainly on images close in time to the training ones, while the networks with less inputs reveal a better generalization performance when applied to independent data sets. In order to increase the network's generalization capability and to select the most significant MODIS channels, a pruning algorithm has been implemented. The pruning outcomes revealed that channel sensitive to ash parameters correspond to the thermal infrared, visible and mid-infrared spectral ranges. The neural network approach has been proven to be effective when addressing the inversion problem for the estimation of volcanic ash and SO2 cloud parameters, providing fast and reliable retrievals, important requirements during volcanic crises.

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.

scholarly journals Neural network approach to forecasting the cost of ferroalloy products

2020 ◽  
Vol 63 (1) ◽  
pp. 78-83
Author(s):  
D. V. Sirotin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Negative Impact ◽  
Forecast Model ◽  
Graphical Analysis ◽  
Network Approach ◽  
Neural Network Approach ◽  
Regression Problem ◽  
The Cost

The article notes the increasing role of ferroalloy sub-sector in the qualitative development of metallurgy. Progress predicts of modern metallurgy are difficult in the context of increasing risks of global economic development. The high volatility of domestic producers’ prices for the main ferroalloys also has a negative impact. It is necessary to develop methodological tools for forecasting changes in market prices for metallurgical products with a high degree of accuracy. One of the important areas of application in metallurgy forecasting tools is construction of a model for forecasting the cost of ferroalloy products. It is the main purpose of the study. On the example of constructing a forecast model for changing the price of ferrosilicon, relevance of the neural network approach to forecasting the cost of ferroalloy products was substantiated. As part of the tasks of industry development, the capabilities of neural networks have been poorly studied to date. Formal description of the time series forecasting model based on neural networks is given. When constructing neural networks, any time series problem is represented as a multidimensional regression problem. The main parameters of predictive networks training are highlighted. The average price of ferrosilicon on the Russian market and the prices in the Russian regions were used as input variables. The networks that meet the qualitative criteria of forecasting models were trained. Selection of the networks was carried out taking into account the results of graphical analysis and cross-checking. A neural network model was constructed to predict the change in ferrosilicon price in the short term with high accuracy. This model can be useful in strategic decisions justifying in the activities of industry research institutes and metallurgical enterprises.

scholarly journals COMPUTING OF LYAPUNOV EXPONENTS TECHNIQUES USING NEURAL NETWORKS

2014 ◽  
pp. 93-98
Author(s):  
Vladimir Golovko ◽  
Yury Savitsky
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Neural Networks ◽  
Time Series ◽  
Lyapunov Exponents ◽  
Multilayer Perceptron ◽  
Lyapunov Spectrum ◽  
Scalar Time Series

The authors examine neural network techniques for computing of Lyapunov spectrum using observations from unknown dynamical system. Such an approach is based on 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 scalar time series. The results of experiments are discussed.

scholarly journals CODEX, a neural network approach to explore signaling dynamics landscapes

2020 ◽  
Author(s):  
Marc-Antoine Jacques ◽  
Maciej Dobrzyński ◽  
Paolo Armando Gagliardi ◽  
Raphael Sznitman ◽  
Olivier Pertz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Single Cell ◽  
Relevant Information ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fluorescent Biosensors ◽  
Signaling Dynamics

AbstractFluorescent biosensors routinely yield thousands of single-cell, heterogeneous, multi-dimensional signaling trajectories that are difficult to mine for relevant information. We present CODEX, an approach based on artificial neural networks to guide exploration of time-series datasets and to identify motifs in dynamic signaling states.

scholarly journals MODELING THE POVERTY LEVEL IN THE RUSSIAN FEDERATION BASED ON A NEURAL NETWORK APPROACH

2021 ◽  
Vol 9 (3) ◽  
pp. 142-156
Author(s):  
M. Karlova ◽  
E. Ryazanceva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Russian Federation ◽  
Annual Data ◽  
Poverty Level ◽  
Neural Network Approach ◽  
Multiple Sampling ◽  
The Neural Network ◽  
The Russian Federation

The article raises the question of modeling the level of poverty as one of the most important socio-economic indicators. A review of publications by domestic and foreign scientists-economists proves the relevance of the topic chosen for the study. Today, the time series apparatus acts as one of the popular tools for studying the dynamics of the poverty level and the factors that directly influence it, but classical statistical forecasting methods impose rather strict assumptions on the construction of models. The article discusses the possibility of using automated neural networks of the STATISTICA package for analyzing and forecasting a time series composed of annual data reflecting the dynamics of the poverty level in the Russian Federation over the past 20 years. The study took into account the strengths and weaknesses of the use of the neural network apparatus for predicting socio-economic processes. The construction of economic and mathematical models was carried out by building automated neural networks, custom neural networks and the method of multiple sampling. When choosing the most preferable model, a multidimensional criterion was used. The comparison of the real poverty level with the values obtained using the models is made, the quality assessment of the developed models is calculated, the poverty level forecast for 2021-2022 is constructed.

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
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