Stirling engine optimization using artificial neural networks algorithm

Neural networks are a type of machine learning algorithm that inspired by the structure and function human brain. They consist layers interconnected “neurons” which process transmit information. can learn to perform variety tasks being trained on large datasets, they have been successfully applied wide range problems, including image speech recognition, natural language processing (NLP), predictive modeling. In this paper, we combining Stirling engines with neural in order improve performance efficiency engine using optimize their operation. For example, an optimizing network such as Multi-Layer Perceptron (MLP) could be predict most efficient operating conditions for based design parameters displacer stroke, phase angle working frequency. Additionally, used diagnose failures engines, potentially improving reliability reducing maintenance costs.