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An optimizing method of RBF neural network based on genetic algorithm

机译：基于遗传算法的RBF神经网络优化方法

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摘要

In the traditional learning algorithms of radial basis function (RBF) neural network, the architecture of the network is hard to be decided; thereby, the learning ability and generalization ability are hard to achieve optimal. In this paper, we propose an algorithm to optimize the RBF neural network learning based on genetic algorithm; it uses hybrid encoding method, that is, encodes the network by binary encoding and encodes the weights by real encoding; the network architecture is self-adapted adjusted, and the weights are learned. Then, the network is further adjusted by pseudo inverse method or least mean square method. Experiments prove that the network gotten by this method has a better architecture and stronger classification ability, and the time of constructing the network artificially is saved. The algorithm is a self-adapted and intelligent learning algorithm.
机译：在传统的径向基函数（RBF）神经网络学习算法中，很难确定网络的体系结构。因此，学习能力和泛化能力很难达到最佳。本文提出了一种基于遗传算法的RBF神经网络学习优化算法。它使用混合编码方法，即通过二进制编码对网络进行编码，并通过实数编码对权重进行编码。网络架构是自适应调整的，权重也得到了学习。然后，通过伪逆方法或最小均方方法进一步调整网络。实验证明，该方法得到的网络具有较好的架构和较强的分类能力，节省了人工构建网络的时间。该算法是一种自适应的智能学习算法。

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