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A Novel Method for Output Characteristics Calculation of Electromagnetic Devices using Multi-kernel RBF Neural Network

机译:一种新的多核RBF神经网络输出特性计算电磁装置的新方法

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摘要

The action performance and reliability of electromagnetic devices is critical to the entire working system. In this paper, a new method for calculating the output characteristics of electromagnetic devices is proposed. This method uses the multi-kernel radial basis function neural network (MK-RBFNN) approximation modeling by the finite element calculation results at the key nodes. It obtains the output response of the electromagnetic device under different coil voltages and air gaps. The key of establishing a MK-RBFNN is to obtain the weight coefficients of each single-kernel radial basis function (RBF) model by using a heuristic weighting strategy. When the electromagnetic output characteristics is calculated in the optimization design of the electromagnetic device, this method solves the problem that the traditional method is difficult to balance the calculation accuracy and speed. The effectiveness of the method is verified by the calculation results of the electromagnetic torque of a typical electromagnetic relay.
机译:电磁器件的动作性能和可靠性对整个工作系统至关重要。本文提出了一种计算电磁器件输出特性的新方法。该方法使用多核径向基函数神经网络(MK-RBFNN)近似建模在关键节点处的有限元计算结果。它获得了不同线圈电压和空气间隙下电磁器件的输出响应。建立MK-RBFNN的关键是通过使用启发式加权策略来获得每个单核径向基函数(RBF)模型的权重系数。当在电磁器件的优化设计中计算电磁输出特性时,该方法解决了传统方法难以平衡计算精度和速度的问题。通过典型电磁继电器的电磁扭矩的计算结果来验证该方法的有效性。

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