...
首页> 外文期刊>Optical memory & neural networks >Neural Network Based Semi-Empirical Models for Dynamical Systems Described by Differential-Algebraic Equations
【24h】

Neural Network Based Semi-Empirical Models for Dynamical Systems Described by Differential-Algebraic Equations

机译:微分-代数方程描述的基于神经网络的动力系统半经验模型

获取原文
获取原文并翻译 | 示例
           

摘要

We analyzed the problem of mathematical modeling and computer simulation of nonlinear controlled dynamical systems usually described by differential-algebraic equations. The problem is proposed to be solved in the framework of the semi-empirical approach combining theoretical knowledge for the plant with training tools of artificial neural network field. The results are presented for a semi-empirical model that simulate the reentry hypersonic vehicle and confirm the efficiency of this approach.
机译:我们分析了通常由微分-代数方程描述的非线性控制动力系统的数学建模和计算机仿真问题。提出了在半经验方法的框架内解决该问题的方法,该方法将植物的理论知识与人工神经网络领域的训练工具相结合。为半经验模型提供了结果,该模型模拟了再入超音速飞行器并确认了这种方法的效率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号