Chaotic System Design Based on Recurrent Artificial Neural Network for the Simulation of EEG Time Series

2022 ◽  
pp. 1510-1521
Author(s):  
Lei Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Chaotic System ◽  
Research Work ◽  
Euler Method ◽  
Open Loop ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Dynamic Time

Electroencephalogram (EEG) signals captured from brain activities demonstrate chaotic features, and can be simulated by nonlinear dynamic time series outputs of chaotic systems. This article presents the research work of chaotic system generator design based on artificial neural network (ANN), for studying the chaotic features of human brain dynamics. The ANN training performances of Nonlinear Auto-Regressive (NAR) model are evaluated for the generation and prediction of chaotic system time series outputs, based on varying the ANN architecture and the precision of the generated training data. The NAR model is trained in open loop form with 1,000 training samples generated using Lorenz system equations and the forward Euler method. The close loop NAR model is used for the generation and prediction of Lorenz chaotic time series outputs. The training results show that better training performance can be achieved by increasing the number of feedback delays and the number of hidden neurons, at the cost of increasing the computational load.

Chaotic System Design Based on Recurrent Artificial Neural Network for the Simulation of EEG Time Series

2019 ◽  
Vol 13 (1) ◽  
pp. 25-35
Author(s):  
Lei Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Chaotic System ◽  
Research Work ◽  
Euler Method ◽  
Open Loop ◽  
Training Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Electroencephalogram (EEG) signals captured from brain activities demonstrate chaotic features, and can be simulated by nonlinear dynamic time series outputs of chaotic systems. This article presents the research work of chaotic system generator design based on artificial neural network (ANN), for studying the chaotic features of human brain dynamics. The ANN training performances of Nonlinear Auto-Regressive (NAR) model are evaluated for the generation and prediction of chaotic system time series outputs, based on varying the ANN architecture and the precision of the generated training data. The NAR model is trained in open loop form with 1,000 training samples generated using Lorenz system equations and the forward Euler method. The close loop NAR model is used for the generation and prediction of Lorenz chaotic time series outputs. The training results show that better training performance can be achieved by increasing the number of feedback delays and the number of hidden neurons, at the cost of increasing the computational load.

Univariate Artificial Neural Network in Forecasting Demand of Low Cost House in Petaling Jaya

2012 ◽  
pp. 1-16 ◽  
Author(s):  
Norhisham Bakhary ◽  
Khairulzan Yahya ◽  
Chin Nam Ng
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Housing Demand ◽  
Percentage Error ◽  
Potential Applications ◽  
Low Cost Housing ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kebelakangan ini ramai penyelidik mendapati ‘Artificial Neural Network’ (ANN) untuk digunakan dalam berbagai bidang kejuruteraan awam. Banyak aplikasi ANN dalam proses peramalan menghasilkan kejayaan. Kajian ini memfokuskan kepada penggunaan siri masa ‘Univariate Neural Network’ untuk meramalkan permintaan rumah kos rendah di daerah Petaling Jaya, Selangor. Dalam kajian ini, beberapa kes bagi sesi latihan dan ramalan telah dibuat untuk mendapatkan model terbaik bagi meramalkan permintaan rumah. Nilai RMSE yang paling rendah yang diperolehi bagi tahap validasi adalah 0.560 dan nilai MAPE yang diperolehi adalah 8.880%. Hasil kajian ini menunjukkan kaedah ini memberikan keputusan yang boleh diterima dalam peramalan permintaan rumah berdasarkan data masa lalu. Kata kunci: Univariate Neural Network, permintaan rumah kos rendah, RMSE, MAPE Recently researchers have found the potential applications of Artificial Neural Network (ANN) in various fields in civil engineering. Many attempts to apply ANN as a forecasting tool has been successful. This paper highlighted the application of Time Series Univariate Neural Network in forecasting the demand of low cost house in Petaling Jaya district, Selangor, using historical data ranging from February 1996 to Appril 2000. Several cases of training and testing were conducted to obtain the best neural network model. The lowest Root Mean Square Error (RMSE) obtained for validation step is 0.560 and Mean Absolute Percentage Error (MAPE) is 8.880%. These results show that ANN is able to provide reliable result in term of forecasting the housing demand based on previous housing demand record. Key words: Time Series Univariate Neural Network, low cost housing demand, RMSE, MAPE

Effective Implementation of Value Engineering Using Artificial Neural Network Aid of Optimization Techniques

2019 ◽  
Vol 27 (03) ◽  
pp. 1950022 ◽  
Author(s):  
M. Prem Swarup ◽  
A. Prabhu Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Research Work ◽  
Optimization Techniques ◽  
Value Engineering ◽  
Training Algorithms ◽  
Air Conditioner ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Value Engineering (VE) is a method for characterizing the developed requirements of a product, and it is concerned with the selection of less excessive conditions. VE can understand and improve the optimal outcome such as quantity, security, unwavering quality and convertibility of each managerial unit. It is an incredible solving tool that can diminish costs while preserving or improving performance and quality requirements. In this research work, VE is presented to calculate the heating cost and cooling cost of the air conditioner with the assistance of an Artificial Neural Network (ANN) with an optimization model. This ANN model effectively chooses the maximum number of sources obtainable and the source respective method with low functional cost and energy consumption. For improving the prediction accuracy of VE in the ANN model, we have incorporated some training algorithms and optimized the network hidden layer and hidden neuron by Opposition Genetic Algorithm (OGA). From the results, trained ANN with OGA predicts the output with 96.02% accuracy and also minimum errors compared with the existing GA process.

Prediction of Life of Compound Die Using Artificial Neural Network (ANN)

2014 ◽  
Vol 622-623 ◽  
pp. 664-671 ◽  
Author(s):  
Sachin Kashid ◽  
Shailendra Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sheet Metal ◽  
Research Work ◽  
Fem Simulation ◽  
Fem Analysis ◽  
Ann Model ◽  
Sheet Metal Parts ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Prediction of life of compound die is an important activity usually carried out by highly experienced die designers in sheet metal industries. In this paper, research work involved in the prediction of life of compound die using artificial neural network (ANN) is presented. The parameters affecting life of compound die are investigated through FEM analysis and the critical simulation values are determined. Thereafter, an ANN model is developed using MATLAB. This ANN model is trained from FEM simulation results. The proposed ANN model is tested successfully on different compound dies designed for manufacturing sheet metal parts. A sample run of the proposed ANN model is also demonstrated in this paper.

scholarly journals Spatiotemporal monthly rainfall reconstruction via artificial neural network – case study: south of Brazil

2007 ◽  
Vol 10 ◽  
pp. 67-76 ◽  
Author(s):  
P. S. Lucio ◽  
F. C. Conde ◽  
I. F. A. Cavalcanti ◽  
A. I. Serrano ◽  
A. M. Ramos ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Low Frequency ◽  
Reconstruction Method ◽  
Ann Model ◽  
Monthly Total ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
South Of Brazil

Abstract. Climatological records users, frequently, request time series for geographical locations where there is no observed meteorological attributes. Climatological conditions of the areas or points of interest have to be calculated interpolating observations in the time of neighboring stations and climate proxy. The aim of the present work is the application of reliable and robust procedures for monthly reconstruction of precipitation time series. Time series is a special case of symbolic regression and we can use Artificial Neural Network (ANN) to explore the spatiotemporal dependence of meteorological attributes. The ANN seems to be an important tool for the propagation of the related weather information to provide practical solution of uncertainties associated with interpolation, capturing the spatiotemporal structure of the data. In practice, one determines the embedding dimension of the time series attractor (delay time that determine how data are processed) and uses these numbers to define the network's architecture. Meteorological attributes can be accurately predicted by the ANN model architecture: designing, training, validation and testing; the best generalization of new data is obtained when the mapping represents the systematic aspects of the data, rather capturing the specific details of the particular training set. As illustration one takes monthly total rainfall series recorded in the period 1961–2005 in the Rio Grande do Sul – Brazil. This reliable and robust reconstruction method has good performance and in particular, they were able to capture the intrinsic dynamic of atmospheric activities. The regional rainfall has been related to high-frequency atmospheric phenomena, such as El Niño and La Niña events, and low frequency phenomena, such as the Pacific Decadal Oscillation.

scholarly journals Artificial Neural Network for analyzing the chaotic time series motion: The case of the Lebanese GDP

2021 ◽  
Author(s):  
Jean-François Verne
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Phase Space ◽  
Regression Models ◽  
Chaotic Dynamic ◽  
Linear Regression Models ◽  
Large Fluctuations ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract In this paper, we propose to analyze the motion of the Lebanese GDP over the period 1950-2019. This macroeconomic aggregate reveals large fluctuations notably during the civil war period (1975-1990). By estimating the Lyapunov exponents with the Artificial Neural Network (ANN) procedure, we show that this series exhibits a strange attractor generated by a chaotic dynamic and we use the embedding procedure to shed in light the bizarre structure of such a series. Thus, the ANN method gives better results regarding prediction than other linear regression models and allows to fit with accuracy the chaotic motion followed by the Lebanese GDP in the phase space.

Prediction of Life of Die Block Using Artificial Neural Network

2014 ◽  
Vol 592-594 ◽  
pp. 689-693 ◽  
Author(s):  
Sachin Kashid ◽  
Shailendra Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Research Work ◽  
Fem Simulation ◽  
Fem Analysis ◽  
Die Design ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Important Activity

Prediction of life of die block is an important activity of die design usually carried out by highly experienced die designers in sheet metal industries. In this paper, research work involved in the prediction of life of die block of compound die using artificial neural network (ANN) is presented. The parameters affecting life of die block are investigated through Finite Element Method (FEM) analysis and the critical simulation values are determined. Thereafter, an ANN model is developed using MATLAB. This ANN model is trained from FEM simulation results. The proposed ANN model is tested successfully on different die block. A sample run of the proposed ANN model is also demonstrated in this paper.

scholarly journals Predicting the Land Value using Regression Techniques and Artificial Neural Network

2019 ◽  
Vol 9 (1S3) ◽  
pp. 431-436
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tamil Nadu ◽  
Research Work ◽  
Land Value ◽  
Regression Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Cost ◽  
Cost Of Construction

Land value can be an important factor which influences the cost of construction on working in the project. The land has socio-economic and environmental values and the confronted problems on land involves the increasing costs for developing the land such as built up, agricultural, residential, commercial and industrial areas. Hence this paper concentrates on prediction of land value by considering some important factors that affects it. The study area has been selected under Tirupur district, being a developing one in Tamil Nadu. The eleven areas in four different taluks under Tirupur district were chosen for research work. The average values of monthly variation are taken for the chosen factor for the years from 2001 to 2017. Using regression analysis and artificial neural network, the prediction has been done for the future land value. The performance of both the model executed good and fit for forecasting results. Though both the model showed better results, Artificial Neural Network (ANN) showed accuracy than regression method.

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