- 4.2Impact Factor
- 7.5CiteScore
- 17 daysTime to First Decision
Computers, Volume 12, Issue 11
November 2023 - 24 articles
Cover Story: Artificial neural networks are parametric machine learning models that are widely used nowadays in a huge range of applications. Moreover, global optimization techniques such as particle swarm optimization or genetic algorithms can be used to effectively train them. However, in order for the optimization techniques to be effective, knowledge of the domain range of the network parameters is also required, which is not always available. Here, an innovative two-step method is proposed for efficient training of artificial neural networks. In the first stage, using grammatical evolution and partitioning and expansion rules, the definition field of the parameters is determined, and in the second stage, the network is trained within the field of definition identified in the first stage. View this paper
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Computers - ISSN 2073-431X