Optimization of Stirling engine design parameters using neural networks

This paper presents a new optimization procedure for Stirling engines based on neural network concepts. Based on modeling and experimental data an intelligent and fast method is proposed which finds the best values for different design variables. Design variables of Stirling engine are optimized using Multi-Layer Perceptron (MLP) neural networks. The optimization procedure is performed for three typical design variables for a given precision which has the capability to be extended for various types of engines and designs.

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