ANN assisted optimization of a multimode linearly tapered bimorph PYT-5 cantilever beam for low frequency energy harvesting
The data on the cantilever's dimensions are considered as input and the output resonant frequency of the first mode and the generated power are considered as output. They are used for training the artificial neural network (ANN) that would provide us with the fitting function that we modify (scalarize) and use in algorithms for the optimization aiming to achieve minimal resonant frequency and maximal generated power. Two methods were used for the training of the ANN, Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG). For optimization we used the goal attainment method (GAM) and genetic algorithm (GA).