基于人工神经网络的振荡分数阶动力系统最优伪阻尼和元阻尼预测
Artificial Neural Network Based Prediction of Optimal Pseudo-Damping and Meta-Damping in Oscillatory Fractional Order Dynamical Systems
AI总结：
本文利用遗传算法和人工神经网络，研究分数阶动力系统中伪阻尼和元阻尼的近似与预测。
摘要：
本文研究了分数阶动力系统中的典型行为，如阻尼振荡。这种响应是由于在某些特殊类别的分数阶系统中存在所谓的伪阻尼和元阻尼。这里，使用遗传算法将分数阶系统中的这种阻尼振荡近似为传统的整数阶阻尼和时间常数。然后，利用基于遗传算法的结果训练多层前馈人工神经网络，以根据分数阶动力系统中的最大阶数或项数预测最优伪阻尼和元阻尼。
英文摘要：
This paper investigates typical behaviors like damped oscillations in fractional order (FO) dynamical systems. Such response occurs due to the presence of, what is conceived as, pseudo-damping and meta-damping in some special class of FO systems. Here, approximation of such damped oscillation in FO systems with the conventional notion of integer order damping and time constant has been carried out using Genetic Algorithm (GA). Next, a multilayer feed-forward Artificial Neural Network (ANN) has been trained using the GA based results to predict the optimal pseudo and meta-damping from knowledge of the maximum order or number of terms in the FO dynamical system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
