An investigation of artificial neural network structure and its effects on the estimation of the low‐cycle fatigue parameters of various steels

Artificial neural networks (ANNs) are a widely used machine learning approach for estimating low-cycle fatigue parameters. ANN structure has its parameters such as hidden layers, neurons, activation functions, training and so forth, these have significant influence over the results. Three layer combinations, neurons ranging from 1 to 25, different functions like hyperbolic tangent sigmoid (tansig), logistic (logsig), linear (purelin) were used, their effects on parameter estimation investigated determine optimal structure. Based results, suggestions regarding of transition life presented. For output most suitable function was tansig. The neuron range been found between 4 9. network with one determined be in terms less knowledge, structural complexity, computational time power.