Tuning of the structure and parameters of a neural network using an improved genetic algorithm
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
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DOI：
10.1109/tnn.2002.804317
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发表时间：
2003-01-01
影响因子：
--
通讯作者：
Tam, PKS
中科院分区：
文献类型：
--
作者：
Leung, FHF;Lam, HK;Tam, PKS
本文提出了一种改进的遗传算法（GA）的神经网络的结构和参数的调整。它也将被证明，改进的遗传算法比标准遗传算法的基础上，一些基准测试功能。本文提出了一种在神经网络的连接中引入开关的神经网络。通过这样做，建议的神经网络可以学习的输入输出关系的应用程序和网络结构使用改进的遗传算法。隐藏节点的数量是手动选择的，从一个小的数字，直到学习性能的适应值是足够好。通过太阳黑子预测和联想记忆的应用实例，说明了改进遗传算法和神经网络的优点。
This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It will also be shown that the improved GA performs better than the standard GA based on some benchmark test functions. A neural network with switches introduced to its link s is proposed. By doing this, the proposed neural network can learn both the input-output relationships of an application and the network structure using the improved GA. The number of hidden nodes is chosen manually by increasing it from a small number until the learning performance in terms of fitness value is good enough. Application examples on sunspot forecasting and associative memory are given to show the merits of the improved GA and the proposed neural network.