In the algorithm, the radial-basis function neural network (RBFNN) is utilized as forward model, and the IGLSA is used to solve the optimization problem in the inverse problem.

该算法中，径向基函数神经网络(RBFNN)用作前向模型，IGLSA用于求解逆问题中的优化问题。

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Afterward, its output is estimated by radial basis function neural network (RBFNN) for extracting SEP features.

后级使用径向基神经网络作信号拟合，提取SEP信号的特征。

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The method of radial basis function neural network (RBFNN) is given to correct the nonlinear errors of the sensors. A BP neural network has been developed to solve the same problem for comparison.

提出了传感器非线性误差校正的径向基函数(RBF)神经网络方法，并与采用BP神经网络校正非线性误差进行了比较。

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The method of radial basis function neural network (RBFNN) is given to correct the nonlinear errors of the sensors. A BP neural network has been developed to solve the same problem for comparison.

提出了传感器非线性误差校正的径向基函数(RBF)神经网络方法，并与采用BP神经网络校正非线性误差进行了比较。

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