The nonlinear Choke Parameter Identification based on the Modified Artificial Neural Network

I. Orlovskyi, M. Fajfer
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Abstract

The method of identification of the Nonlinear Choke Parameter (NCP) identification via a Modified artificial Recurrent Neural Network (MRNN) has been proposed and researched. The method makes it possible to use knowledge about the object for the synthesis of an MRNN structure and the computation of their coefficients from measured instantaneous values of currents and voltages in the circuit. NCP in the proposed MRNN were realized by expanding the input signal space of the network, using the normalized signals of polynomial terms. The proposed method can estimate MRNN structure to meets with NCP accuracy requirements. The NCP approximation in the one and a few function forms were also provided. The proposed method of NCP identification is based on the weighted MRNN coefficients. A comparison is made of the accuracy of identification of NCP for different MRNN structures and previously known measurement object data. An accuracy estimation for the models NCP in the form of a MRNN was made on mathematical models and on a real object.
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基于改进人工神经网络的非线性扼流圈参数辨识
提出并研究了一种基于改进人工递归神经网络(MRNN)的非线性阻流参数辨识方法。该方法可以利用对目标的了解来合成MRNN结构,并根据电路中测量到的电流和电压的瞬时值计算它们的系数。该方法利用多项式项的归一化信号扩展网络的输入信号空间,实现了神经网络的NCP。该方法能够估计出满足NCP精度要求的MRNN结构。给出了一种和几种函数形式下的NCP近似。提出了一种基于加权MRNN系数的NCP识别方法。比较了不同MRNN结构和已知测量对象数据对NCP的识别精度。分别在数学模型和实际对象上对MRNN形式的NCP模型进行了精度估计。
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