Serial artificial neural networks characterized by Gaussian mixture for the modelling of the Consigma25 continuous manufacturing line

AlAlaween, Wafa’ H.; Mahfouf, Mahdi; Omar, Chalak; Al-Asady, Riyadh B.; Monaco, Daniele; Salman, Agba D.

doi:10.1016/j.powtec.2023.119296

Cited by 3 publications

(1 citation statement)

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“…Artificial intelligence (AI) refers to the simulation of human intelligence in computer systems designed to solve problems in a human way [7]. AI systems are designed to analyze data, recognize patterns, make decisions, and improve over time through iterative learning processes or decision-making.…”

Section: Introductionmentioning

confidence: 99%

Validation Challenges in Data for Different Diesel Engine Performance Regimes Utilising HVO Fuel: A Study on the Application of Artificial Neural Networks for Emissions Prediction

Matijošius,

Rimkus,

Gruodis

2024

Machines

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Artificial neural networks (ANNs) provide supervised learning via input pattern assessment and effective resource management, thereby improving energy efficiency and predicting environmental fluctuations. The advanced technique of ANNs forecasts diesel engine emissions by collecting measurements during trial sessions. This study included experimental sessions to establish technical and ecological indicators for a diesel engine across several operational scenarios. VALLUM01, a novel tool, has been created with a user-friendly interface for data input/output, intended for the purposes of testing and prediction. There was a comprehensive collection of 12 input parameters and 10 output parameters that were identified as relevant and sufficient for the objectives of training, validation, and prediction. The proper value ranges for transforming into fuzzy sets for input/output to an ANN were found. Given that the ANN’s training session comprises 1,000,000 epochs and 1000 perceptrons within a single-hidden layer, its effectiveness can be considered high. Many statistical distributions, including Pearson, Spearman, and Kendall, validate the prediction accuracy. The accuracy ranges from 96% on average, and in some instances, it may go up to 99%.

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Section: Introductionmentioning

confidence: 99%