Estimation of Power System Harmonic Using Modified Normalized Least Mean Square

2015 ◽  
Vol 785 ◽  
pp. 378-382 ◽  
Author(s):  
H.M.M. Alhaj ◽  
N.M. Nor ◽  
Vijanth S. Asirvadam ◽  
M.F. Abdullah ◽  
T. Ibrahim
Keyword(s):  
Power System ◽  
Gradient Descent ◽  
Stochastic Gradient Descent ◽  
Mean Square ◽  
Least Mean Square ◽  
Linear Element ◽  
Gradient Descent Algorithm ◽  
Normalized Least Mean Square ◽  
Adaptive Power ◽  
Harmonic Components

A new adaptive power system harmonic estimator is presented, which is competent of tracking power system harmonic components. The proposed estimator technique is based on the normalized Least Mean Square (LMS), which is a stochastic gradient descent algorithm. The learning method of the proposed estimator is based upon the recursive estimate of the signal power, and is faster tracking of harmonic components as compared to the introduced Adaptive Linear Element (ADALINE).

MapReduce and Optimized Deep Network for Rainfall Prediction in Agriculture

2020 ◽  
Vol 63 (6) ◽  
pp. 900-912
Author(s):  
Oswalt Manoj S ◽  
Ananth J P
Keyword(s):  
Deep Learning ◽  
Gradient Descent ◽  
Prediction Models ◽  
Short Term Memory ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Mean Square ◽  
Rainfall Prediction ◽  
Gradient Descent Algorithm ◽  
Major Factors

Abstract Rainfall prediction is the active area of research as it enables the farmers to move with the effective decision-making regarding agriculture in both cultivation and irrigation. The existing prediction models are scary as the prediction of rainfall depended on three major factors including the humidity, rainfall and rainfall recorded in the previous years, which resulted in huge time consumption and leveraged huge computational efforts associated with the analysis. Thus, this paper introduces the rainfall prediction model based on the deep learning network, convolutional long short-term memory (convLSTM) system, which promises a prediction based on the spatial-temporal patterns. The weights of the convLSTM are tuned optimally using the proposed Salp-stochastic gradient descent algorithm (S-SGD), which is the integration of Salp swarm algorithm (SSA) in the stochastic gradient descent (SGD) algorithm in order to facilitate the global optimal tuning of the weights and to assure a better prediction accuracy. On the other hand, the proposed deep learning framework is built in the MapReduce framework that enables the effective handling of the big data. The analysis using the rainfall prediction database reveals that the proposed model acquired the minimal mean square error (MSE) and percentage root mean square difference (PRD) of 0.001 and 0.0021.

scholarly journals Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm

2021 ◽  
Author(s):  
Marco Mele ◽  
Cosimo Magazzino ◽  
Nicolas Schneider ◽  
Floriana Nicolai
Keyword(s):  
Economic Growth ◽  
Gradient Descent ◽  
Statistical Data ◽  
Stochastic Gradient Descent ◽  
Policy Recommendations ◽  
Dynamic Interactions ◽  
Gradient Descent Algorithm ◽  
The Relationship ◽  
Main Strand ◽  
Yearly Data

AbstractAlthough the literature on the relationship between economic growth and CO2 emissions is extensive, the use of machine learning (ML) tools remains seminal. In this paper, we assess this nexus for Italy using innovative algorithms, with yearly data for the 1960–2017 period. We develop three distinct models: the batch gradient descent (BGD), the stochastic gradient descent (SGD), and the multilayer perceptron (MLP). Despite the phase of low Italian economic growth, results reveal that CO2 emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO2 increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided.

scholarly journals Optical Recognition of Handwritten Logic Formulas Using Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2761
Author(s):  
Vaios Ampelakiotis ◽  
Isidoros Perikos ◽  
Ioannis Hatzilygeroudis ◽  
George Tsihrintzis
Keyword(s):  
Neural Networks ◽  
Character Recognition ◽  
Gradient Descent ◽  
Feedforward Neural Networks ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Training Algorithms ◽  
Gradient Descent Algorithm ◽  
Two Stages ◽  
And Training

In this paper, we present a handwritten character recognition (HCR) system that aims to recognize first-order logic handwritten formulas and create editable text files of the recognized formulas. Dense feedforward neural networks (NNs) are utilized, and their performance is examined under various training conditions and methods. More specifically, after three training algorithms (backpropagation, resilient propagation and stochastic gradient descent) had been tested, we created and trained an NN with the stochastic gradient descent algorithm, optimized by the Adam update rule, which was proved to be the best, using a trainset of 16,750 handwritten image samples of 28 × 28 each and a testset of 7947 samples. The final accuracy achieved is 90.13%. The general methodology followed consists of two stages: the image processing and the NN design and training. Finally, an application has been created that implements the methodology and automatically recognizes handwritten logic formulas. An interesting feature of the application is that it allows for creating new, user-oriented training sets and parameter settings, and thus new NN models.

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