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。
This paper presents the concept of knowledge transformation coefficient, learning ability coefficient and knowledge rigidity etc.
本文提出了知识转化系数、学习能力系数和知识刚度等概念，并相应建立了竞争能力的评价模型。
After exploratory researches for the structure and learning algorithm of neural network, an algorithm based on adaptive gain coefficient is presented.
对神经网络的结构和学习算法进行了探索性研究，引入一种基于自适应增益系数改进的学习算法。
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