A hybrid learning approach is presented in which genetic algorithms are used to optimize both the network architecture and the regularization coefficient.
提出了一种利用遗传算法优化前向神经网络的结构和正则项系数的混合学习算法。
Because of defects of BP algorithm, a hybrid learning algorithm is applied to train and optimize the network parameters.
针对BP算法的不足，使用混合学习算法训练网络，优化了网络参数。
Because of defects of BP algorithm, a hybrid learning algorithm is applied to train and optimize the network parameters.
针对BP算法的不足，使用混合学习算法训练网络，优化了网络参数。
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