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 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.

Dynamic Ridge Polynomial Higher Order Neural Network

2010 ◽  
pp. 255-268 ◽  
Author(s):  
Rozaida Ghazali ◽  
Abir Hussain ◽  
Nazri Mohd Nawi
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Neural Networks ◽  
Time Series ◽  
Dynamical Systems ◽  
Dynamic Structure ◽  
Higher Order ◽  
System Control ◽  
Internal States ◽  
The Brain

This chapter proposes a novel Dynamic Ridge Polynomial Higher Order Neural Network (DRPHONN). The architecture of the new DRPHONN incorporates recurrent links into the structure of the ordinary Ridge Polynomial Higher Order Neural Network (RPHONN) (Shin & Ghosh, 1995). RPHONN is a type of feedforward Higher Order Neural Network (HONN) (Giles & Maxwell, 1987) which implements a static mapping of the input vectors. In order to model dynamical functions of the brain, it is essential to utilize a system that is capable of storing internal states and can implement complex dynamic system. Neural networks with recurrent connections are dynamical systems with temporal state representations. The dynamic structure approach has been successfully used for solving varieties of problems, such as time series forecasting (Zhang & Chan, 2000; Steil, 2006), approximating a dynamical system (Kimura & Nakano, 2000), forecasting a stream flow (Chang et al, 2004), and system control (Reyes et al, 2000). Motivated by the ability of recurrent dynamic systems in real world applications, the proposed DRPHONN architecture is presented in this chapter.

scholarly journals Stacked Heterogeneous Neural Networks for Time Series Forecasting

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Florin Leon ◽  
Mihai Horia Zaharia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Case Studies ◽  
Multilayer Perceptron ◽  
Neural Model ◽  
Activation Function ◽  
Time Series Forecasting ◽  
The Other ◽  
Activation Functions

A hybrid model for time series forecasting is proposed. It is a stacked neural network, containing one normal multilayer perceptron with bipolar sigmoid activation functions, and the other with an exponential activation function in the output layer. As shown by the case studies, the proposed stacked hybrid neural model performs well on a variety of benchmark time series. The combination of weights of the two stack components that leads to optimal performance is also studied.

Neural Networks for the Tasks of Analytical Support of Public Health and Environment Monitoring Systems

2021 ◽  
pp. 23-30
Author(s):  
BI Marchenko ◽  
NK Plugotarenko ◽  
OA Semina
Keyword(s):  
Public Health ◽  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Monitoring System ◽  
Multilayer Perceptron ◽  
Malignant Neoplasms ◽  
Artificial Neural

Introduction: Ensuring a further improvement of efficiency of the public health monitoring system requires integration of the modern health risk analysis methodology with a complex of adapted unified traditional and innovative analytical methods and data exchange with the environmental monitoring system. Objectives: The study aimed to test and assess the accuracy of predicting the incidence of malignant neoplasms using an artificial neural network. Materials and methods: The analyzed time series are presented by information from statistical reporting forms on malignant neoplasms in the city of Taganrog, Rostov Region. We applied a regression model and a forecasting modeling technique based on a feedforward artificial neural network of a multilayer perceptron type. An artificial neural network with 117 neurons in a hidden layer was created in the environment of the Matlab R2021a application package with a set of tools for the synthesis and analysis of neural networks Neural Network Toolbox using the Levenberg-Marquardt algorithm for its learning. Results: Approbation of two forecasting models was carried out on learning samples of different duration including 15 and 34 years. In a comparative assessment of the accuracy of forecasts for 2018 and 2019, absolute and relative errors were estimated. The accuracy of the neural network forecasting model was higher than that of the regression model both for the total of malignant neoplasms and for most cancer sites. The absolute errors of forecasts for 2018 when using regression and neural network models were 17.05 and 1.49 per 100,000 population, for 2019 – 39.07 and 4.42, respectively. The prediction accuracy dropped with a decrease in the time series duration and an increase in the distance from the boundaries of the learning sample. Conclusions: The feedforward artificial neural network of the multilayer perceptron type provides more accurate predictions using minimal input information compared to the regression model, which is its undoubted advantage.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

scholarly journals System for Forecasting COVID-19 Cases Using Time-Series and Neural Networks Models

2021 ◽  
Vol 5 (1) ◽  
pp. 46
Author(s):  
Mostafa Abotaleb ◽  
Tatiana Makarovskikh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Models ◽  
Third Wave ◽  
The Third ◽  
Modeling And Forecasting ◽  
Auto Regressive ◽  
The Russian Federation ◽  
R Programming

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation.

scholarly journals Nonlinear Time-Series Analysis of Pulsation of Post-AGB Stars by Genetic Algorithm/Neural Network Hybrid Systems

2000 ◽  
Vol 176 ◽  
pp. 135-136
Author(s):  
Toshiki Aikawa
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Hybrid System ◽  
Nonlinear Time Series ◽  
Photometric Data ◽  
Nonlinear Time Series Analysis ◽  
Agb Stars

AbstractSome pulsating post-AGB stars have been observed with an Automatic Photometry Telescope (APT) and a considerable amount of precise photometric data has been accumulated for these stars. The datasets, however, are still sparse, and this is a problem for applying nonlinear time series: for instance, modeling of attractors by the artificial neural networks (NN) to the datasets. We propose the optimization of data interpolations with the genetic algorithm (GA) and the hybrid system combined with NN. We apply this system to the Mackey–Glass equation, and attempt an analysis of the photometric data of post-AGB variables.

scholarly journals Predicting Financial Prices of Stock Market using Recurrent Convolutional Neural Networks

2020 ◽  
Vol 12 (6) ◽  
pp. 21-32
Author(s):  
Muhammad Zulqarnain ◽  
◽  
Rozaida Ghazali ◽  
Muhammad Ghulam Ghouse ◽  
Yana Mazwin Mohmad Hassim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Financial Time ◽  
Data Mining Approach ◽  
Learning Tasks ◽  
Financial Prices ◽  
Stock Indexes

Financial time-series prediction has been long and the most challenging issues in financial market analysis. The deep neural networks is one of the excellent data mining approach has received great attention by researchers in several areas of time-series prediction since last 10 years. “Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for financial predictions. In this paper, we proposed to combine architectures, which exploit the advantages of CNN and RNN simultaneously, for the prediction of trading signals. Our model is essentially presented to financial time series predicting signals through a CNN layer, and directly fed into a gated recurrent unit (GRU) layer to capture long-term signals dependencies. GRU model perform better in sequential learning tasks and solve the vanishing gradients and exploding issue in standard RNNs. We evaluate our model on three datasets for stock indexes of the Hang Seng Indexes (HSI), the Deutscher Aktienindex (DAX) and the S&P 500 Index range 2008 to 2016, and associate the GRU-CNN based approaches with the existing deep learning models. Experimental results present that the proposed GRU-CNN model obtained the best prediction accuracy 56.2% on HIS dataset, 56.1% on DAX dataset and 56.3% on S&P500 dataset respectively.

scholarly journals Prediction of the chrome distribution in subarctic Noyabrsk using co-kriging, generalized regression neural network, multilayer perceptron, and hybrid technics

2019 ◽  
pp. 77-86
Author(s):  
A. G. Buevich ◽  
I. E. Subbotina ◽  
A. V. Shichkin ◽  
A. P. Sergeev ◽  
E. M. Baglaeva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multilayer Perceptron ◽  
Surface Soil ◽  
Soil Layer ◽  
Generalized Regression Neural Network ◽  
Residual Kriging ◽  
Surface Soil Layer ◽  
Artificial Neural ◽  
Generalized Regression

Combination of geostatistical interpolation (kriging) and machine learning (artificial neural networks, ANN) methods leads to an increase in the accuracy of forecasting. The paper considers the application of residual kriging of an artificial neural network to predicting the spatial contamination of the surface soil layer with chromium (Cr). We reviewed and compared two neural networks: the generalized regression neural network (GRNN) and multilayer perceptron (MLP), as well as the combined method: multilayer perceptron residual kriging (MLPRK). The study is based on the results of the screening of the surface soil layer in the subarctic Noyabrsk, Russia. The models are developed based on computer modeling with minimization of the RMSE. The MLPRK model showed the best prognostic accuracy.

Methods for Multi-Step Time Series Forecasting Neural Networks

2011 ◽  
pp. 226-250 ◽  
Author(s):  
Douglas M. Kline
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Iterative Method ◽  
Time Series Forecasting ◽  
Independent Method ◽  
Data Series ◽  
Operating Characteristics ◽  
Quarterly Data ◽  
Joint Method

In this study, we examine two methods for Multi-Step forecasting with neural networks: the Joint Method and the Independent Method. A subset of the M-3 Competition quarterly data series is used for the study. The methods are compared to each other, to a neural network Iterative Method, and to a baseline de-trended de-seasonalized naïve forecast. The operating characteristics of the three methods are also examined. Our findings suggest that for longer forecast horizons the Joint Method performs better, while for short forecast horizons the Independent Method performs better. In addition, the Independent Method always performed at least as well as or better than the baseline naïve and neural network Iterative Methods.

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