A fast and efficient algorithm for training radial basis function neural networks based on a fuzzy partition of the input space
A fast and efficient algorithm for training radial basis function neural networks based on a fuzzy partition of the input space
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DOI：
10.1021/ie010263h
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发表时间：
2002-02-20
影响因子：
4.2
通讯作者：
Bafas, G
中科院分区：
文献类型：
--
作者：
Sarimveis, H;Alexandridis, A;Bafas, G
流行的径向基函数（RBF）神经网络架构和一种新的快速有效的网络训练方法用于对非线性动态多输入多输出（MIMO）离散时间系统进行建模。所提出的训练方法基于输入空间的模糊划分，并结合了自组织和监督学习。通过使用模拟和实验数据开发神经网络模型来说明该算法。结果表明，与用于训练 RBF 网络的标准技术相比，该方法速度更快，并且生成的模型更准确。另一个重要的优点是，对于输入空间的给定模糊划分，所提出的方法能够确定正确的网络结构，而无需使用试错过程。
The popular radial basis function (RBF) neural network architecture and a new fast and efficient method for training such a network are used to model nonlinear dynamical multi-input multi-output (MIMO) discrete-time systems. The proposed training methodology is based on a fuzzy partition of the input space and combines self-organized and supervised learning. The algorithm is illustrated through the development of neural network models using simulated and experimental data. Results show that the methodology is much faster and produces more accurate models compared to the standard techniques used to train RBF networks. Another important advantage is that, for a given fuzzy partition of the input space, the proposed method is able to determine the proper network structure, without using a trial and error procedure.