scholarly journals Wind speed forecasting using neural networks

2019 ◽  
Vol 44 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Tyler Blanchard ◽  
Biswanath Samanta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
The United States ◽  
Weather Data ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

The prediction of wind speed is critical in the assessment of feasibility of a potential wind turbine site. This work presents a study on prediction of wind speed using artificial neural networks. Two variations of artificial neural networks, namely, nonlinear autoregressive neural network and nonlinear autoregressive neural network with exogenous inputs, were used to predict wind speed utilizing 1 year of hourly weather data from four locations around the United States to train, validate, and test these networks. This study optimized both neural network configurations and it demonstrated that both models were suitable for wind speed prediction. Both models outperformed persistence model (with a factor of about 2 to 10 in root mean square error ratio). Both artificial neural network models were implemented for single-step and multi-step-ahead prediction of wind speed for all four locations and results were compared. Nonlinear autoregressive neural network with exogenous inputs model gave better prediction performance than nonlinear autoregressive model and the difference was statistically significant.

Artificial Neural Networks

2011 ◽  
pp. 222-243
Author(s):  
Joarder Kamruzzaman ◽  
Ruhul Sarker
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Neural Network Models ◽  
Network Applications ◽  
The Neural Network ◽  
Artificial Neural ◽  
Neural Network Applications

The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is therefore to provide a brief review of artificial neural network applications in finance and manufacturing areas.

Comparing Conventional and Artificial Neural Network Models for the Pricing of Options

2002 ◽  
pp. 220-235 ◽  
Author(s):  
Paul Lajbcygier
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Parameter ◽  
Network Models ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Conventional Models ◽  
Artificial Neural Network Models

The pricing of options on futures is compared using conventional models and artificial neural networks. This work demonstrates superior pricing accuracy using the artificial neural networks in an important subset of the input parameter set.

Artificial Neural Networks

2006 ◽  
pp. 1-27 ◽  
Author(s):  
Joarder Kamruzzaman ◽  
Ruhul A. Sarker
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Neural Network Models ◽  
Network Applications ◽  
The Neural Network ◽  
Artificial Neural ◽  
Neural Network Applications

The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is therefore to provide a brief review of artificial neural network applications in finance and manufacturing areas.

scholarly journals Artificial neural networks incorporating cost significant Items towards enhancing estimation for (life-cycle) costing of construction projects

2013 ◽  
Vol 13 (3) ◽  
pp. 51-64 ◽  
Author(s):  
Ayedh Alqahtani ◽  
Andrew Whyte
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Life Cycle ◽  
Life Cycle Cost ◽  
Construction Projects ◽  
Back Propagation ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Excel Solver

Industrial application of life-cycle cost analysis (LCCA) is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client) the advantages to be gained from objective (LCCA) comparison of (sub)component material specifications. To address the need for a user-friendly structured approach to facilitate complex processing, the work described here develops a new, accessible framework for LCCA of construction projects; it acknowledges Artificial Neural Networks (ANNs) to compute the whole-cost(s) of construction and uses the concept of cost significant items (CSI) to identify the main cost factors affecting the accuracy of estimation. ANNs is a powerful means to handle non-linear problems and subsequently map between complex input/output data, address uncertainties. A case study documenting 20 building projects was used to test the framework and estimate total running costs accurately. Two methods were used to develop a neural network model; firstly a back-propagation method was adopted (using MATLAB SOFTWARE); and secondly, spread-sheet optimisation was conducted (using Microsoft Excel Solver). The best network was established as consisting of 19 hidden nodes, with the tangent sigmoid used as a transfer function of NNs model for both methods. The results find that in both neural network models, the accuracy of the developed NNs model is 1% (via Excel-solver) and 2% (via back-propagation) respectively.

scholarly journals Artificial neural networks and the study of evolution of prey coloration

2007 ◽  
Vol 362 (1479) ◽  
pp. 421-430 ◽  
Author(s):  
Sami Merilaita
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
Visual Information ◽  
Visual Complexity ◽  
Network Models ◽  
Neural Network Models ◽  
General Terms ◽  
Artificial Neural

In this paper, I investigate the use of artificial neural networks in the study of prey coloration. I briefly review the anti-predator functions of prey coloration and describe both in general terms and with help of two studies as specific examples the use of neural network models in the research on prey coloration. The first example investigates the effect of visual complexity of background on evolution of camouflage. The second example deals with the evolutionary choice of defence strategy, crypsis or aposematism. I conclude that visual information processing by predators is central in evolution of prey coloration. Therefore, the capability to process patterns as well as to imitate aspects of predator's information processing and responses to visual information makes neural networks a well-suited modelling approach for the study of prey coloration. In addition, their suitability for evolutionary simulations is an advantage when complex or dynamic interactions are modelled. Since not all behaviours of neural network models are necessarily biologically relevant, it is important to validate a neural network model with empirical data. Bringing together knowledge about neural networks with knowledge about topics of prey coloration would provide a potential way to deepen our understanding of the specific appearances of prey coloration.

scholarly journals Automatic migraine classification using artificial neural networks

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 618
Author(s):  
Paola A. Sanchez-Sanchez ◽  
José Rafael García-González ◽  
Juan Manuel Rúa Ascar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Neural Network Models ◽  
Accuracy And Precision ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Selection Of

Background: Previous studies of migraine classification have focused on the analysis of brain waves, leading to the development of complex tests that are not accessible to the majority of the population. In the early stages of this pathology, patients tend to go to the emergency services or outpatient department, where timely identification largely depends on the expertise of the physician and continuous monitoring of the patient. However, owing to the lack of time to make a proper diagnosis or the inexperience of the physician, migraines are often misdiagnosed either because they are wrongly classified or because the disease severity is underestimated or disparaged. Both cases can lead to inappropriate, unnecessary, or imprecise therapies, which can result in damage to patients’ health. Methods: This study focuses on designing and testing an early classification system capable of distinguishing between seven types of migraines based on the patient’s symptoms. The methodology proposed comprises four steps: data collection based on symptoms and diagnosis by the treating physician, selection of the most relevant variables, use of artificial neural network models for automatic classification, and selection of the best model based on the accuracy and precision of the diagnosis. Results: The artificial neural network models used provide an excellent classification performance, with accuracy and precision levels >97% and which exceed the classifications made using other model, such as logistic regression, support vector machines, nearest neighbor, and decision trees. Conclusions: The implementation of migraine classification through artificial neural networks is a powerful tool that reduces the time to obtain accurate, reliable, and timely clinical diagnoses.

scholarly journals Speech Command Recognition using Artificial Neural Networks

2020 ◽  
Vol 4 (2) ◽  
pp. 73
Author(s):  
Sushan Poudel ◽  
Dr. R Anuradha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Recognition System ◽  
Neural Network Models ◽  
Artificial Neural

Speech is one of the most effective way for human and machine to interact. This project aims to build Speech Command Recognition System that is capable of predicting the predefined speech commands. Dataset provided by Google’s TensorFlow and AIY teams is used to implement different Neural Network models which include Convolutional Neural Network and Recurrent Neural Network combined with Convolutional Neural Network. The combination of Convolutional and Recurrent Neural Network outperforms Convolutional Neural Network alone by 8% and achieved 96.66% accuracy for 20 labels.

scholarly journals Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames

2019 ◽  
Vol 21 (1) ◽  
pp. 151-168 ◽  
Author(s):  
Opeoluwa Owoyele ◽  
Prithwish Kundu ◽  
Muhsin M Ameen ◽  
Tarek Echekki ◽  
Sibendu Som
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Dimensional Manifold ◽  
Neural Network Models ◽  
Compression Ignition Engine ◽  
Memory Footprint ◽  
Lookup Tables ◽  
Artificial Neural

The “curse of dimensionality” has limited the applicability and expansion of tabulated combustion models. While the tabulated flamelet model and other multi-dimensional manifold approaches have shown predictive capability, the associated tabulation involves the storage of large lookup tables, requiring large memory as well as multi-dimensional interpolation subroutines, all implemented during runtime. This work investigates the use of deep artificial neural networks to replace lookup tables in order to reduce the memory footprint and increase the computational speed of tabulated flamelets and related approaches. Specifically, different strategic approaches to training the artificial neural network models are explored and a grouped multi-target artificial neural network is introduced, which takes advantage of the ability of artificial neural networks to map an input space to multiple targets by classifying the species based on their correlation to one another. The grouped multi-target artificial neural network approach is validated by applying it to an n-dodecane spray flame using conditions of the Spray A flame from the Engine Combustion Network and comparing global flame characteristics for different ambient conditions using a well-established large-eddy simulation framework. The same framework is then extended to the simulations of methyl decanoate combustion in a compression ignition engine. The validation studies show that the grouped multi-target artificial neural networks are able to accurately capture flame liftoff, autoignition, two-stage heat release and other quantitative trends over a range of conditions. The use of neural networks in conjunction with the grouping mechanism as performed in the grouped multi-target artificial neural network produces a significant reduction in the memory footprint and computational costs for the code and, thus, widens the operating envelope for higher fidelity engine simulations with detailed mechanisms.

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 Modeling of Harmonic Current in Electrical Grids with Photovoltaic Power Integration Using a Nonlinear Autoregressive with External Input Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 4015
Author(s):  
Adán Alberto Jumilla-Corral ◽  
Héctor Enrique Campbell-Ramírez ◽  
Carlos Perez-Tello ◽  
Zulma Yadira Medrano-Hurtado ◽  
Pedro Mayorga-Ortiz ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
External Input ◽  
Electric Power Supply ◽  
Photovoltaic Power ◽  
Electrical Grids ◽  
The Neural Network ◽  
Artificial Neural ◽  
Nonlinear Autoregressive

This research presents the modeling and prediction of the harmonic behavior of current in an electric power supply grid with the integration of photovoltaic power by inverters using artificial neural networks to determine if the use of the proposed neural network is capable of capturing the harmonic behavior of the photovoltaic energy integrated into the user's electrical grids. The methodology used was based on the use of recurrent artificial neural networks of the nonlinear autoregressive with external input type. Work data were obtained from experimental sources through the use of a test bench, measurement, acquisition, and monitoring equipment. The input–output parameters for the neural network were the current values in the inverter and the supply grid, respectively. The results showed that the neural network can capture the dynamics of the analyzed system. The generated model presented flexibility in data handling, allowing to represent and predict the behavior of the harmonic phenomenon. The obtained algorithm can be transferred to physical or virtual systems for the control or reduction of harmonic distortion.

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