scholarly journals Flood routing modelling with Artificial Neural Networks

2006 ◽  
Vol 9 ◽  
pp. 131-136 ◽  
Author(s):  
R. Peters ◽  
G. Schmitz ◽  
J. Cullmann
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Performance ◽  
Flood Routing ◽  
Hydrodynamic Modelling ◽  
Feedforward Networks ◽  
One Dimensional ◽  
Data Resolution ◽  
Artificial Neural ◽  
The One

Abstract. For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN). First studies show the ability of adequately trained multilayer feedforward networks (MLFN) to reproduce the model performance.

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

Revealing the importance of international and domestic cooperation by using artificial neural networks: case of European radical and incremental innovators

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Viktor Prokop ◽  
Jan Stejskal ◽  
Beata Mikusova Merickova ◽  
Samuel Amponsah Odei
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
International Cooperation ◽  
Incremental Innovation ◽  
Content Type ◽  
Innovation Activities ◽  
Design Activities ◽  
Artificial Neural ◽  
The One ◽  
Innovation Processes

PurposeThe purpose of this study is to introduce innovative ideas into the treatment of the radical and incremental innovations and to fill the research gap by using: (1) methods that can perform complicated tasks and solve complex problems leading in creation of radical and incremental innovation and (2) a broad sample of firms across countries. The authors’ ambition is to contribute to the scientific knowledge by producing evidence about the novel usage of artificial neural network techniques for measuring European firms' innovation activities appearing in black boxes of innovation processes.Design/methodology/approachIn this study, the authors incorporate an international context into Chesbrough's open innovation (OI) theory and, on the one hand, support the hypothesis that European radical innovators benefit more from foreign cooperation than incremental innovators. On the other hand, the results of the analyses show that European incremental innovators rely on domestic cooperation supported by cooperation with foreign public research institutes. Moreover, the use of decision trees (DT) allows the authors to reveal specific patterns of successful innovators emerging within the hidden layers of neural networks.FindingsThe authors prove that radical European innovators using either internal or external R&D strategies, while the combinations of these strategies do not bring successful innovation outputs. In contrast, European incremental innovators benefit from various internal R&D processes in which engagement in design activities plays a crucial role.Originality/valueThe authors introduce innovative ideas into the treatment of hidden innovation processes and measuring the innovation performance (affected by domestic or international cooperation) of European firms. The approach places emphasis on the novelty of innovation and the issue of international cooperation in the era of OI by designing the framework using a combination of artificial neural networks and DT.

MODELING OF NEURAL PREDICTIVE CONTROL OF IRRIGATION MACHINES

2021 ◽  
pp. 14-22
Author(s):  
G. N. KAMYSHOVA ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Predictive Control ◽  
Intelligent Control ◽  
Machine Learning Algorithms ◽  
Intelligent Control Systems ◽  
Artificial Neural ◽  
The One ◽  
Neural Predictive Control

The purpose of the study is to develop new scientific approaches to improve the efficiency of irrigation machines. Modern digital technologies allow the collection of data, their analysis and operational management of equipment and technological processes, often in real time. All this allows, on the one hand, applying new approaches to modeling technical systems and processes (the so-called “data-driven models”), on the other hand, it requires the development of fundamentally new models, which will be based on the methods of artificial intelligence (artificial neural networks, fuzzy logic, machine learning algorithms and etc.).The analysis of the tracks and the actual speeds of the irrigation machines in real time showed their significant deviations in the range from the specified speed, which leads to a deterioration in the irrigation parameters. We have developed an irrigation machine’s control model based on predictive control approaches and the theory of artificial neural networks. Application of the model makes it possible to implement control algorithms with predicting the response of the irrigation machine to the control signal. A diagram of an algorithm for constructing predictive control, a structure of a neuroregulator and tools for its synthesis using modern software are proposed. The versatility of the model makes it possible to use it both to improve the efficiency of management of existing irrigation machines and to develop new ones with integrated intelligent control systems.

scholarly journals Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 588
Author(s):  
Felipe Leite Coelho da Silva ◽  
Kleyton da Costa ◽  
Paulo Canas Rodrigues ◽  
Rodrigo Salas ◽  
Javier Linkolk López-Gonzales
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Approach ◽  
Electricity Consumption ◽  
Industrial Sector ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
The One ◽  
Mlp Model

Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.

Artificial Neural Networks to Improve Current Harmonics Identification and Compensation

2010 ◽  
pp. 256-290 ◽  
Author(s):  
Patrice Wira ◽  
Djaffar Ould Abdeslam ◽  
Jean Mercklé
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Digital Signal Processor ◽  
Power Distribution ◽  
Distribution System ◽  
Control Laws ◽  
Identification Schemes ◽  
Learning Capabilities ◽  
Artificial Neural ◽  
The One

Artificial Neural Networks (ANNs) have demonstrated very interesting properties in adaptive identification schemes and control laws. In this work, they are employed for the on-line control strategy of an Active Power Filter (APF) in order to improve its performance. Indeed, neural-based approaches are synthesized to design adaptive and efficient harmonic identification schemes. The proposed neural approaches are employed for compensating for the changing harmonic distortions introduced in a power distribution system by unknown nonlinear loads. The implementation of the ANNs has been optimized on a digital signal processor for real-time experiments. The feasibility of the implementation has been validated and the neural compensation schemes exhibit good performances compared to conventional approaches. By their learning capabilities, ANNs are able to take into account time-varying parameters such as voltage sags and harmonic content changes, and thus appreciably improve the performance of the APF compared to the one obtained with traditional compensating methods.

EXTENDED DAILY EXCHANGE RATES FORECASTS USING WAVELET TEMPORAL RESOLUTIONS

2005 ◽  
Vol 01 (01) ◽  
pp. 79-107 ◽  
Author(s):  
MAK KABOUDAN
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Random Walk ◽  
Genetic Programming ◽  
Exchange Rates ◽  
Us Dollar ◽  
Artificial Neural ◽  
Japanese Yen ◽  
The One

Applying genetic programming and artificial neural networks to raw as well as wavelet-transformed exchange rate data showed that genetic programming may have good extended forecasting abilities. Although it is well known that most predictions of exchange rates using many alternative techniques could not deliver better forecasts than the random walk model, in this paper employing natural computational strategies to forecast three different exchange rates produced two extended forecasts (that go beyond one-step-ahead) that are better than naïve random walk predictions. Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions. Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen exchange rate also using genetic programming outperformed the sixteen-step-ahead random walk predictions of the exchange rate. However, random walk predictions of the US dollar/British pound exchange rate outperformed all forecasts obtained using genetic programming. Random walk predictions of the same three exchange rates employing raw and wavelet-transformed data also outperformed all forecasts obtained using artificial neural networks.

Prediction of 2-D Lift Motions Using Artificial Neural Networks: An Evolutionary Approach

2005 ◽  
Vol 49 (14) ◽  
pp. 1405-1409
Author(s):  
Miguel A. Perez ◽  
Maury A. Nussbaum
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Model Performance ◽  
Human Movement ◽  
Two Dimensional ◽  
Modeling Paradigm ◽  
Neural Network Structure ◽  
Artificial Neural

Movement prediction is an important aspect of human simulation, where more efficient and accurate models are needed. Artificial neural networks could potentially serve as a modeling option in this realm. This investigation evaluates the performance of a particular artificial neural network structure in modeling sagittally symmetric two-dimensional lifting and lowering movements. Model performance was evaluated using three training datasets, each consisting of distinct representation levels of the overall dataset. Results are discussed in terms of their practical meaning, and suggestions for future improvements in the modeling scheme are provided. Overall, artificial neural networks show promise as a modeling paradigm for the prediction of human movement.

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