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).本数据集将悬臂梁的尺寸数据作为输入，将一阶模态谐振频率与发电功率作为输出，用于训练人工神经网络（Artificial Neural Network，ANN）。该网络可生成拟合函数，我们对该函数进行标量化处理后，将其应用于优化算法，以实现谐振频率最小化与发电功率最大化的目标。本次训练共采用两种方法：莱文贝格-马夸特算法（Levenberg-Marquardt，LM）与缩放共轭梯度算法（Scaled Conjugate Gradient，SCG）；优化环节则采用目标达成法（Goal Attainment Method，GAM）与遗传算法（Genetic Algorithm，GA）。