scholarly journals TIME SERIES FORECASTING USING ARTIFICIAL NEURAL NETWORK WITH EXTENDED ADAPTIVE LEARNING

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Mr. Dhanaji Vilas Mirajkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Learning Rate ◽  
Experimental Result ◽  
Adaptive Learning Rate ◽  
Artificial Neural

Artificial neural network (ANN) mainly consists of learning algorithms, which are require to optimize the convergence of neural networks. We need to optimize the convergence of neural networks in order to improve the speed and accuracy of decision making process. To enable the optimization process one of the widely used algorithm is back propagation learning algorithm. Objective of study is to applied backpropagation algorithm for solving multivariate time series problem. To better the accuracy of neural network it is important to find optimized architecture for the problem under consideration. The learning rate is also an important factor which affects the performance of result. In this study, we proposed extended adaptive learning approach in which learning rate is adapted from number of previous iteration error trend in first half of training. In next half of training learning rate is adapted as per adaptive learning rate algorithm. Compare performance of three variation of backpropagationalgorithm. All these variation experimented on two standard dataset. Experimental result shows that during validation and training ANN with extended adaptive learning rate outperforms other than two variations.

Prediction of cyanobacterial blooms in the Dau Tieng Reservoir using an artificial neural network

2017 ◽  
Vol 68 (11) ◽  
pp. 2070 ◽  
Author(s):  
Manh-Ha Bui ◽  
Thanh-Luu Pham ◽  
Thanh-Son Dao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Transfer Functions ◽  
Learning Algorithm ◽  
Oxygen Demand ◽  
Cyanobacterial Blooms ◽  
Cyanobacteria Bloom ◽  
Feed Forward Back Propagation ◽  
Artificial Neural

An artificial neural network (ANN) model was used to predict the cyanobacteria bloom in the Dau Tieng Reservoir, Vietnam. Eight environmental parameters (pH, dissolved oxygen, temperature, total dissolved solids, total nitrogen (TN), total phosphorus, biochemical oxygen demand and chemical oxygen demand) were introduced as inputs, whereas the cell density of three cyanobacteria genera (Anabaena, Microcystis and Oscillatoria) with microcystin concentrations were introduced as outputs of the three-layer feed-forward back-propagation ANN. Eighty networks covering all combinations of four learning algorithms (Bayesian regularisation (BR), gradient descent with momentum and adaptive learning rate, Levenberg–Mardquart, scaled conjugate gradient) with two transfer functions (tansig, logsig) and 10 numbers of hidden neurons (6–16) were trained and validated to find the best configuration fitting the observed data. The result is a network using the BR learning algorithm, tansig transfer function and nine neurons in the hidden layer, which shows satisfactory predictions with the low values of error (root mean square error=0.108) and high correlation coefficient values (R=0.904) between experimental and predicted values. Sensitivity analysis on the developed ANN indicated that TN and temperature had the most positive and negative effects respectively on microcystin concentrations. These results indicate that ANN modelling can effectively predict the behaviour of the cyanobacteria bloom process.

scholarly journals Analisis Error dan Epoch dengan Pengembangan Adaptive Learning Rate dan Parameter Momentum pada Metode Backpropagation

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Zahratul Fitri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Learning Rate ◽  
Backpropagation Algorithm ◽  
Adaptive Learning Rate ◽  
Best Value ◽  
Artificial Neural Network Ann ◽  
Parameter Values ◽  
Value Of Learning

Abstrak— Algoritma backpropagation merupakan bagian dari Jaringan Syaraf Tiruan (JST) yang memiliki beberapa layar tersembunyi. Algoritma backpropagation juga merupakan multi-layer yang banyak digunakan untuk menyelesaikan persoalan yang luas, akan tetapi, algoritma backpropagation juga memiliki kelamahan pada proses pembelajaran yang cukup lambat. Pada penelitian ini penulis menganalisis bagaimana mengembangkan algoritma backpropagation dengan menggunakan learning rate dan parameter momentum untuk meminimalisir error dan epoch yang akurat sebagai proses menghitung perubahan bobot. Dari hasil penelitian diperoleh bahwa pengembangan yang dilakukan memperoleh nilai paling baik pada nilai momentum yaitu 0,9 dan 1.0 dan nilai learning rate yaitu > 0,7. Hal ini membuktikan bahwa nilai pembelajaran dengan menggunakan nilai parameter momentum dan nilai learning rate diatas sangat baik digunakan sebagai percepatan laju konvergensi.Kata kunci— Algoritma Backpropagation, Parameter Momentum, Adaptive Learning Rate . Abstract— Backpropagation algorithm is part of an Artificial Neural Network (ANN), which has some hidden screen. Backpropagation algorithm is also a multi-layer finish that is widely used for large problems, however, the backpropagation algorithm also has weaknesses in the learning process is quite slow. In this study the authors analyze how to develop a backpropagation algorithm using learning rate and momentum parameters to minimize the error and accurate epoch as the process of calculating the weight change. The result showed that the development is carried out to obtain best value on the momentum value of 0.9 and 1.0 and the value of learning rate is> 0.7. It is proved that the value of learning by using the parameter values of momentum and learning rate values above are best used as a convergence rate acceleration.Keywords— Backpropagation Algorithm, Parameter of Momentum, Adaptive   Learning Rate

scholarly journals Using GIS and Artificial Neural Network for Deforestation Prediction

2018 ◽  
Author(s):  
Vahid Ahmadi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Spatial Data ◽  
Satellite Images ◽  
Learning Algorithm ◽  
Geospatial Information ◽  
Environmental Diversity ◽  
Artificial Neural ◽  
Main Components

Geospatial Information Systems (GIS) can provide a great environment for using machine learning algorithm for spatial data such as satellite images. Integrating this functionality with artificial intelligence algorithms for analyzing spatial data enables us to predict challenging disasters such as deforestation. Deforestation as an environmental problems has been recorded the most serious threat to environmental diversity and one of the main components of land-use change. In this paper, we investigate spatial distribution of deforestation using artificial neural networks and satellite imagery. We modeled deforestation process using various factors in determining the relationship between deforestation and environmental and socioeconomic factors. Hence, for this purpose, the proximity to roads and habitats, fragmentation of the forest, height from sea level, slope, and soil type are considered in the model. In this research, we modeled land cover changes (forests) to predict deforestation using an artificial neural network due to its significant potential for the development of nonlinear complex models. The procedure involves image registration and error correction, image classification, preparing deforestation maps, determining layers, and designing a multi-layer neural network to predict deforestation. The satellite images for this study are of a region in Hong Kong which are captured from 2012 to 2016. The results of the study demonstrate that neural networks approach for predicting deforestation can be utilized and its outcomes show the areas that destroyed during the research period.

scholarly journals An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting

2018 ◽  
Vol 8 (2) ◽  
pp. 121-132 ◽  
Author(s):  
Esra Akdeniz ◽  
Erol Egrioglu ◽  
Eren Bas ◽  
Ufuk Yolcu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Real Life ◽  
Life Time ◽  
High Order ◽  
Non Linear ◽  
Artificial Neural

Abstract Real-life time series have complex and non-linear structures. Artificial Neural Networks have been frequently used in the literature to analyze non-linear time series. High order artificial neural networks, in view of other artificial neural network types, are more adaptable to the data because of their expandable model order. In this paper, a new recurrent architecture for Pi-Sigma artificial neural networks is proposed. A learning algorithm based on particle swarm optimization is also used as a tool for the training of the proposed neural network. The proposed new high order artificial neural network is applied to three real life time series data and also a simulation study is performed for Istanbul Stock Exchange data set.

scholarly journals ARTIFICIAL NEURAL NETWORK FOR MODELS OF HUMAN OPERATOR

2017 ◽  
Vol 12 ◽  
pp. 99
Author(s):  
Martin Ruzek
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Learning Algorithm ◽  
Learning Rule ◽  
Human Operator ◽  
Biological Neural Network ◽  
Artificial Learning ◽  
Artificial Neural

This paper presents a new approach to mental functions modeling with the use of artificial neural networks. The artificial neural networks seems to be a promising method for the modeling of a human operator because the architecture of the ANN is directly inspired by the biological neuron. On the other hand, the classical paradigms of artificial neural networks are not suitable because they simplify too much the real processes in biological neural network. The search for a compromise between the complexity of biological neural network and the practical feasibility of the artificial network led to a new learning algorithm. This algorithm is based on the classical multilayered neural network; however, the learning rule is different. The neurons are updating their parameters in a way that is similar to real biological processes. The basic idea is that the neurons are competing for resources and the criterion to decide which neuron will survive is the usefulness of the neuron to the whole neural network. The neuron is not using "teacher" or any kind of superior system, the neuron receives only the information that is present in the biological system. The learning process can be seen as searching of some equilibrium point that is equal to a state with maximal importance of the neuron for the neural network. This position can change if the environment changes. The name of this type of learning, the homeostatic artificial neural network, originates from this idea, as it is similar to the process of homeostasis known in any living cell. The simulation results suggest that this type of learning can be useful also in other tasks of artificial learning and recognition.

scholarly journals Implementasi Jaringan Syaraf Tiruan Recurrent Dengan Metode Pembelajaran Gradient Descent Adaptive Learning Rate Untuk Pendugaan Curah Hujan Berdasarkan Peubah Enso

2010 ◽  
Vol 1 (2) ◽  
pp. 418
Author(s):  
Afan Galih Salman ◽  
Yen Lina Prasetio
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Gradient Descent ◽  
Learning Rate ◽  
Training Data ◽  
Testing Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Data Group

The use of technology of technology Artificial Neural Network (ANN) in prediction of rainfall can be done using the learning approach. ANN prediction accuracy measured by the coefficient of determination (R2) and Root Mean Square Error (RMSE).This research employ a recurrent optimized heuristic Artificial Neural Network (ANN) Recurrent Elman gradient descent adaptive learning rate approach using El-Nino Southern Oscilation (ENSO) variable, namely Wind, Southern Oscillation Index (SOI), Sea Surface Temperatur (SST) dan Outgoing Long Wave Radiation (OLR) to forecast regional monthly rainfall. The patterns of input data affect the performance of Recurrent Elman neural network in estimation process. The first data group that is 75% training data and 25% testing data produce the maximum R2 69.2% at leap 0 while the second data group that is 50% training data & 50% testing data produce the maximum R2 53.6%.at leap 0 Our result on leap 0 is better than leap 1,2 or 3. 

scholarly journals Modeliranje i prognoziranje broja zaposlenih u turizmu i hotelskoj industriji u Republici Hrvatskoj primjenom modela umjetnih neuronskih mreža

2020 ◽  
Vol 10 (2) ◽  
pp. 3-20
Author(s):  
Tea Baldigara
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Percentage Error ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Prognostic Power ◽  
Modelling And Forecasting

The paper investigates the performance and prognostic power of artificial neural network models in modelling and forecasting of time series of seasonal character. Models of artificial neural networks have been applied in modelling and forecasting the monthly total number of employees, the number of employed men and the number of employed women in the activity of providing accommodation services and preparing and serving food and beverages in the Republic of Croatia. The obtained modelling results have been compared with the results obtained by applying some of the traditionally used quantitative models in the analysis of seasonal time series, such as the Holt-Winters model of triple exponential smoothing and the seasonal multiplicative model of exponential trend. The evaluation of the performance and prognostic power of individual models was performed by comparing the average absolute and average absolute percentage error and the correlation coefficient between the actual and estimated values, and the predicted values were compared with the actual values. The evaluation of the obtained results showed that the selected model of acyclic multilayer perceptron is suitable for modelling and forecasting time series of seasonal character. The comparison of prognostic powers and actual and projected values of the number of employees suggests that the designed model of the artificial neural network is very reliable. This indicates that the models of artificial neural networks have great application potentials in the domain of modelling and forecasting of time series of a seasonal character.

scholarly journals Using GIS and Artificial Neural Network for Deforestation Prediction

2018 ◽  
Author(s):  
Vahid Ahmadi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Spatial Data ◽  
Satellite Images ◽  
Learning Algorithm ◽  
Geospatial Information ◽  
Environmental Diversity ◽  
Artificial Neural ◽  
Main Components

Geospatial Information Systems (GIS) can provide a great environment for using machine learning algorithm for spatial data such as satellite images. Integrating this functionality with artificial intelligence algorithms for analyzing spatial data enables us to predict challenging disasters such as deforestation. Deforestation as an environmental problems has been recorded the most serious threat to environmental diversity and one of the main components of land-use change. In this paper, we investigate spatial distribution of deforestation using artificial neural networks and satellite imagery. We modeled deforestation process using various factors in determining the relationship between deforestation and environmental and socioeconomic factors. Hence, for this purpose, the proximity to roads and habitats, fragmentation of the forest, height from sea level, slope, and soil type are considered in the model. In this research, we modeled land cover changes (forests) to predict deforestation using an artificial neural network due to its significant potential for the development of nonlinear complex models. The procedure involves image registration and error correction, image classification, preparing deforestation maps, determining layers, and designing a multi-layer neural network to predict deforestation. The satellite images for this study are of a region in Hong Kong which are captured from 2012 to 2016. The results of the study demonstrate that neural networks approach for predicting deforestation can be utilized and its outcomes show the areas that destroyed during the research period.

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.

scholarly journals Uncertainty encountered when modelling self-excited thermoacoustic oscillations with artificial neural networks

2017 ◽  
Vol 9 (4) ◽  
pp. 367-379 ◽  
Author(s):  
Stefan Jaensch ◽  
Wolfgang Polifke
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Cfd Simulation ◽  
Forced Response ◽  
Data Sets ◽  
Artificial Neural ◽  
Thermoacoustic Oscillations

Artificial neural networks are a popular nonlinear model structure and are known to be able to describe complex nonlinear phenomena. This article investigates the capability of artificial neural networks to serve as a basis for deducing nonlinear low-order models of the dynamics of a laminar flame from a Computational Fluid Dynamics (CFD) simulation. The methodology can be interpreted as an extension of the CFD/system identification approach: a CFD simulation of the flame is perturbed with a broadband, high-amplitude signal and the resulting fluctuations of the global heat release rate and of the reference velocity are recorded. Thereafter, an artificial neural network is identified based on the time series collected. Five data sets that differ in amplitude distribution and length were generated for the present study. Based on each of these data sets, a parameter study was conducted by varying the structure of the artificial neural network. A general fit-value criterion is applied and the 10 artificial neural networks with the highest fit values are selected. Comparing of these 10 artificial neural networks allows to obtain information on the uncertainty encountered. It is found that the methodology allows to capture the forced response of the flame reasonably well. The validation against the forced response, however, depends strongly on the forcing signal used. Therefore, an additional validation criterion is investigated. The artificial neural networks are coupled with a thermoacoustic network model. This allows to model self-excited thermoacoustic oscillations. If the training time series are sufficiently long, this coupled model allows to predict the trend of the root mean square values of fluctuations of the global heat release rate. However, the prediction of the maximal value of the fluctuation amplitude is poor. Another drawback found is that even if very long-time series are available, the behaviour of artificial neural networks cannot be guaranteed. It is concluded that more sophisticated nonlinear low-order models are necessary.

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