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Application of RBF Network in System Identification for Flight Control Systems

机译:RBF网络在飞行控制系统系统识别中的应用

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The flight control system (FCS) is a complex nonlinear multi-input and multi-output) system, it is very difficult to identify the model of this system. The nonlinear relation of I/O data can be expressed by artificial neural network(ANN). The ANN can fit the any function accurately by studying. In this paper, the FCS of a type of fighter is identified by radial basis function (RBF) network. The training algorithm of the RBF network is improved by a grouping optimizing method and a new training algorithm. Simulation results about application of this network with new algorithm were given. The results show that the complex FCS can be identified by artificial neural network accurately.
机译:飞行控制系统(FCS)是一个复杂的非线性多输入和多输出)系统,很难识别该系统的模型。 I / O数据的非线性关系可以由人工神经网络(ANN)表示。 ANN可以通过学习准确地符合任何功能。在本文中,通过径向基函数(RBF)网络来识别一种战斗机的FC。通过分组优化方法和新的训练算法改善了RBF网络的训练算法。给出了关于该网络应用与新算法的仿真结果。结果表明,可以精确地通过人工神经网络识别复杂的FC。

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