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.

scholarly journals Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1070
Author(s):  
Abdul Gani Abdul Jameel
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Functional Groups ◽  
Average Error ◽  
Ann Model ◽  
Oh Groups ◽  
Unseen Data ◽  
Learning Capabilities ◽  
Artificial Neural ◽  
Oxygenated Fuels

The self-learning capabilities of artificial neural networks (ANNs) from large datasets have led to their deployment in the prediction of various physical and chemical phenomena. In the present work, an ANN model was developed to predict the yield sooting index (YSI) of oxygenated fuels using the functional group approach. A total of 265 pure compounds comprising six chemical classes, namely paraffins (n and iso), olefins, naphthenes, aromatics, alcohols, and ethers, were dis-assembled into eight constituent functional groups, namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic –CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups, and ether O groups. These functional groups, in addition to molecular weight and branching index, were used as inputs to develop the ANN model. A neural network with two hidden layers was used to train the model using the Levenberg–Marquardt (ML) training algorithm. The developed model was tested with 15% of the random unseen data points. A regression coefficient (R2) of 0.99 was obtained when the experimental values were compared with the predicted YSI values from the test set. An average error of 3.4% was obtained, which is less than the experimental uncertainty associated with most reported YSI measurements. The developed model can be used for YSI prediction of hydrocarbon fuels containing alcohol and ether-based oxygenates as additives with a high degree of accuracy.

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.

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.

scholarly journals Deep Convolutional Spiking Neural Networks for Image Classification

2021 ◽  
Author(s):  
Ruthvik Vaila
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gradient Descent ◽  
Stochastic Gradient ◽  
Spiking Neural Networks ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Learning Capabilities ◽  
Artificial Neural

Spiking neural networks are biologically plausible counterparts of artificial neural networks. Artificial neural networks are usually trained with stochastic gradient descent (SGD) and spiking neural networks are trained with bioinspired spike timing dependent plasticity (STDP). Spiking networks could potentially help in reducing power usage owing to their binary activations. In this work, we use unsupervised STDP in the feature extraction layers of a neural network with instantaneous neurons to extract meaningful features. The extracted binary feature vectors are then classified using classification layers containing neurons with binary activations. Gradient descent (backpropagation) is used only on the output layer to perform training for classification. Surrogate gradients are proposed to perform backpropagation with binary gradients. The accuracies obtained for MNIST and the balanced EMNIST data set compare favorably with other approaches. The effect of the stochastic gradient descent (SGD) approximations on learning capabilities of our network are also explored. We also studied catastrophic forgetting and its effect on spiking neural networks (SNNs). For the experiments regarding catastrophic forgetting, in the classification sections of the network we use a modified synaptic intelligence that we refer to as cost per synapse metric as a regularizer to immunize the network against catastrophic forgetting in a Single-Incremental-Task scenario (SIT). In catastrophic forgetting experiments, we use MNIST and EMNIST handwritten digits datasets that were divided into five and ten incremental subtasks respectively. We also examine behavior of the spiking neural network and empirically study the effect of various hyperparameters on its learning capabilities using the software tool SPYKEFLOW that we developed. We employ MNIST, EMNIST and NMNIST data sets to produce our results.

scholarly journals Dynamically Positioned Ship Steering Making Use of Backstepping Method and Artificial Neural Networks

2018 ◽  
Vol 25 (4) ◽  
pp. 5-12
Author(s):  
Anna Witkowska ◽  
Tacjana Niksa Rynkiewicz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Time Instant ◽  
Analytical Determination ◽  
Backstepping Method ◽  
Control Laws ◽  
Precise Knowledge ◽  
Artificial Neural ◽  
Network Weight

Abstract The article discusses the issue of designing a dynamic ship positioning system making use of the adaptive vectorial backstepping method and RBF type artificial neural networks. In the article, the backstepping controller is used to determine control laws and neural network weight adaptation laws. The artificial neural network is applied at each time instant to approximate nonlinear functions containing parametric uncertainties. The proposed control system does not require precise knowledge of the model of ship dynamics and external disturbances, it also eliminates the problem of analytical determination of the regression matrix when designing the control law with the aid of the adaptive backstepping procedure.

Sign in / Sign up

Export Citation Format

Share Document