Utilisation of Deep Learning (DL) and Neural Networks (NN) Algorithms for Energy Power Generation: A Social Network and Bibliometric Analysis (2004-2022)
Abdelhamid Zaidi
Abstract:The research landscape on the applications of advanced computational tools (ACTs) such as machine/deep learning and neural network algorithms for energy and power generation (EPG) was critically examined through publication trends and bibliometrics data analysis. The Elsevier Scopus database and the PRISMA methodology were employed to identify and screen the published documents, whereas the bibliometric analysis software VOSviewer was used to analyse the co-authorships, citations, and keyword occurrences. The … Show more
This paper employs BP Neural Network (BPNN) theory to evaluate innovation and entrepreneurship education in universities. It utilizes students' evaluation indexes as input vectors and determines the number of hidden layer neurons. Experimental results serve as output vectors. The BPNN method proves reasonable and feasible for vocational education course evaluation, exhibiting a 14.96% higher accuracy than traditional genetic algorithms. The paper discusses the model, configuration, characteristics, training process, algorithm enhancement, and limitations of neural networks, followed by an introduction to genetic algorithms. Through analysis of principles, basic operations, and common operators, it establishes a theoretical foundation for subsequent discussions.
This paper employs BP Neural Network (BPNN) theory to evaluate innovation and entrepreneurship education in universities. It utilizes students' evaluation indexes as input vectors and determines the number of hidden layer neurons. Experimental results serve as output vectors. The BPNN method proves reasonable and feasible for vocational education course evaluation, exhibiting a 14.96% higher accuracy than traditional genetic algorithms. The paper discusses the model, configuration, characteristics, training process, algorithm enhancement, and limitations of neural networks, followed by an introduction to genetic algorithms. Through analysis of principles, basic operations, and common operators, it establishes a theoretical foundation for subsequent discussions.
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