Interval type-2 fuzzy neural networks for chaotic time series prediction: A concise overview
Chaotic time series widely exists in nature and society (eg, meteorology, physics,
economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent …
economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent …
Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches
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 …
become increasingly important as it can help power companies in better load scheduling …
Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction
Cooperative coevolution decomposes a problem into subcomponents and employs
evolutionary algorithms for solving them. Cooperative coevolution has been effective for …
evolutionary algorithms for solving them. Cooperative coevolution has been effective for …
Evolutionary deep learning-based energy consumption prediction for buildings
Today's energy resources are closer to consumers due to sustainable energy and advanced
technology. To that end, ensuring a precise prediction of energy consumption at the …
technology. To that end, ensuring a precise prediction of energy consumption at the …
Development of support vector regression identification model for prediction of dam structural behaviour
V Ranković, N Grujović, D Divac, N Milivojević - Structural Safety, 2014 - Elsevier
The paper presents the application of support vector regression (SVR) to accurate
forecasting of the tangential displacement of a concrete dam. The SVR nonlinear …
forecasting of the tangential displacement of a concrete dam. The SVR nonlinear …
[HTML][HTML] GA-mADAM-IIoT: A new lightweight threats detection in the industrial IoT via genetic algorithm with attention mechanism and LSTM on multivariate time series …
Abstract The Industrial Internet of Things (IIoT) is undergoing rapid development, and as a
result, security threats have emerged as a significant concern. IIoT networks, while …
result, security threats have emerged as a significant concern. IIoT networks, while …
Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction
Ensembles have been shown to provide better generalization performance than single
models. However, the creation, selection and combination of individual predictors is critical …
models. However, the creation, selection and combination of individual predictors is critical …
Toward a digital twin: time series prediction based on a hybrid ensemble empirical mode decomposition and BO-LSTM neural networks
W Hu, Y He, Z Liu, J Tan… - Journal of …, 2021 - asmedigitalcollection.asme.org
Precise time series prediction serves as an important role in constructing a digital twin (DT).
The various internal and external interferences result in highly nonlinear and stochastic time …
The various internal and external interferences result in highly nonlinear and stochastic time …
Color recurrence plots for bearing fault diagnosis
This paper presents bearing fault diagnosis using the image classification of different fault
patterns. Feature extraction for image classification is carried out using a novel approach of …
patterns. Feature extraction for image classification is carried out using a novel approach of …
On application of machine learning method for history matching and forecasting of times series data from hydrocarbon recovery process using water flooding
M Pal - Petroleum Science and Technology, 2021 - Taylor & Francis
The focus of this paper is on application of advance data analytics and deep machine
learning methods for time series forecasting of injection/production data from subsurface …
learning methods for time series forecasting of injection/production data from subsurface …