Advances in Artificial Neural Networks – Methodological Development and Application

Huang, Yanbo

doi:10.3390/algor2030973

Cited by 160 publications

(95 citation statements)

References 114 publications

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“…The results are shown in Table 2. ANN models are known to overfit the data and show poor generalization when the dataset is large and training times are longer [45]. From the above results, it is seen that ANN does not perform well.…”

Section: Selecting the Machine Learning Benchmark Modelmentioning

confidence: 72%

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

et al. 2018

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Abstract:Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and selecting accurate time series models is a challenging task as this requires training several different models for selecting the best amongst them along with substantial feature engineering to derive informative features and finding optimal time lags, a commonly used input features for time series models. Methods: Our approach uses machine learning and a long short-term memory (LSTM)-based neural network with various configurations to construct forecasting models for short to medium term aggregate load forecasting. The research solves above mentioned problems by training several linear and non-linear machine learning algorithms and picking the best as baseline, choosing best features using wrapper and embedded feature selection methods and finally using genetic algorithm (GA) to find optimal time lags and number of layers for LSTM model predictive performance optimization. Results: Using France metropolitan's electricity consumption data as a case study, obtained results show that LSTM based model has shown high accuracy then machine learning model that is optimized with hyperparameter tuning. Using the best features, optimal lags, layers and training various LSTM configurations further improved forecasting accuracy. Conclusions: A LSTM model using only optimally selected time lagged features captured all the characteristics of complex time series and showed decreased Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for medium to long range forecasting for a wider metropolitan area.

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Section: Selecting the Machine Learning Benchmark Modelmentioning

confidence: 72%

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

et al. 2018

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“…In this work, an enhanced bottleneck neural network (EBNN) topology is used that includes five layers with three hidden layers: mapping layer, bottleneck layer, and de-mapping layer, the input and output variables [13,22,23]. Once the general structure is defined, it remains to determine the necessary number of neurons in each hidden layer.…”

Section: Enhanced Bottleneck Neural Networkmentioning

confidence: 99%

Multivariate Statistical Process Control Using Enhanced Bottleneck Neural Network

Bouzenad

Ramdani

2017

Algorithms

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Abstract:Monitoring process upsets and malfunctions as early as possible and then finding and removing the factors causing the respective events is of great importance for safe operation and improved productivity. Conventional process monitoring using principal component analysis (PCA) often supposes that process data follow a Gaussian distribution. However, this kind of constraint cannot be satisfied in practice because many industrial processes frequently span multiple operating states. To overcome this difficulty, PCA can be combined with nonparametric control charts for which there is no assumption need on the distribution. However, this approach still uses a constant confidence limit where a relatively high rate of false alarms are generated. Although nonlinear PCA (NLPCA) using autoassociative bottle-neck neural networks plays an important role in the monitoring of industrial processes, it is difficult to design correct monitoring statistics and confidence limits that check new performance. In this work, a new monitoring strategy using an enhanced bottleneck neural network (EBNN) with an adaptive confidence limit for non Gaussian data is proposed. The basic idea behind it is to extract internally homogeneous segments from the historical normal data sets by filling a Gaussian mixture model (GMM). Based on the assumption that process data follow a Gaussian distribution within an operating mode, a local confidence limit can be established. The EBNN is used to reconstruct input data and estimate probabilities of belonging to the various local operating regimes, as modelled by GMM. An abnormal event for an input measurement vector is detected if the squared prediction error (SPE) is too large, or above a certain threshold which is made adaptive. Moreover, the sensor validity index (SVI) is employed successfully to identify the detected faulty variable. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms, and is hence expected to better monitor many practical processes.Keywords: multivariate statistical process control (MSPC); bottleneck neural network (BNN); Gaussian mixture model (GMM); adaptive confidence limit (ACL); wastewater treatment plant (WWTP); benchmark simulation model No. 1 (BSM1)

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“…The amplitude values of sub-critical and critical peaks in the CWT are used to feed an Artificial Neural Network (ANN) [43], allowing crack diagnosis, and determination of the position and depth of the crack.…”

Section: Continuous Wavelet Transformmentioning

confidence: 99%

Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors

2016

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Wavelet transform (WT) has been used in the diagnosis of cracked rotors since the 1990s. At present, WT is one of the most commonly used tools to treat signals in several fields. Understandably, this has been an area of extensive scientific research, which is why this paper aims to summarize briefly the major advances in the field since 2008. The present review considers advances in the use and application of WT, the selection of the parameters used, and the key achievements in using WT for crack diagnosis.

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Advances in Artificial Neural Networks – Methodological Development and Application

Cited by 160 publications

References 114 publications

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

Multivariate Statistical Process Control Using Enhanced Bottleneck Neural Network

Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors

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