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Semi-empirical Neural Network Based Approach to Modelling and Simulation of Controlled Dynamical Systems

机译:基于半经验神经网络的受控动力系统建模与仿真方法

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摘要

A modelling and simulation approach is discussed for nonlinear controlled dynamical systems under multiple and diverse uncertainties. The main goal is to demonstrate capabilities for semi-empirical neural network based models combining theoretical domain-specific knowledge with training tools of artificial neural network field. Training of the dynamical neural network model for multi-step ahead prediction is performed in a sequential fashion. Computational experiments are carried out to confirm efficiency of the proposed approach.
机译:讨论了具有多种不确定性的非线性受控动力系统的建模和仿真方法。主要目标是证明将基于理论领域的知识与人工神经网络领域的训练工具相结合的基于半经验神经网络的模型的功能。动态神经网络模型的多步提前预测训练是按顺序进行的。进行计算实验以确认所提出方法的效率。

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