Abstract

We discuss the problem of mathematical and computer modeling of nonlinear controllable dynamical systems with incomplete knowledge about the object of modeling and the conditions of its operation. The suggested approach is based on a merging of theoretical knowledge for the system with training tools of artificial neural network (ANN) field. We present an extension of previously proposed semi-empirical neural network modeling methods for the case of continuous time ANN-models, which makes it possible to expand the possibilities of this approach. The efficiency of this approach is demonstrated using the example of motion modeling for a maneuverable aircraft.

中文翻译：

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基于半经验连续时间神经网络的可控动力系统模型

摘要

我们讨论关于非线性可控动力系统的数学和计算机建模问题，但对建模对象及其运行条件的了解不全面。所建议的方法是基于将系统的理论知识与人工神经网络（ANN）领域的培训工具合并在一起的。对于连续时间的ANN模型，我们提出了先前提出的半经验神经网络建模方法的扩展，这使得扩展这种方法的可能性成为可能。以机动飞机的运动建模为例，证明了这种方法的效率。