Leung F F, Lam H K, Ling S H, Tam P S
Dept. of Electron. and Inf. Eng., Hong Kong Polytech. Univ., Kowloon, China.
IEEE Trans Neural Netw. 2003;14(1):79-88. doi: 10.1109/TNN.2002.804317.
This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It is also 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 links 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.
本文提出了一种使用改进遗传算法（GA）对神经网络的结构和参数进行调优的方法。同时还表明，基于一些基准测试函数，改进的GA比标准GA表现更好。提出了一种在其连接中引入开关的神经网络。通过这样做，所提出的神经网络可以使用改进的GA学习应用程序的输入输出关系以及网络结构。隐藏节点的数量通过从少量开始增加，直到适应度值方面的学习性能足够好来手动选择。给出了太阳黑子预测和联想记忆的应用实例，以展示改进的GA和所提出的神经网络的优点。
